Prompt Engineering means carefully designing, improving, and repeating the input you give to a language model so it produces the output you want. Instead of building new models, you guide an existing model by how you ask it. It's a blend of creativity (how you phrase things) and methodical testing (seeing what works best). Moreover, it isn't about clever wording, it's the key to unlocking the full value of large language models by steering them toward accurate, relevant, and use-case-specific results. By designing the right prompt you improve model precision, tailor its behaviour to your business context. Real-World Example Imagine you're building a chatbot for a FinTech app that helps customers with budgeting. Instead of simply asking: "Explain how to set up a budget," you craft a prompt like: "You are a friendly FinTech assistant. A customer says: 'I want to start saving 20% of my income each month but have irregular freelance payments.' Explain step-by-step they could set up a flexible budget strategy, with examples." Because of the carefully crafted context (role, user scenario, goal) the underlying model responds with a highly tailored answer rather than a generic textbook-explanation. That's prompt engineering in action: guiding the model with the right context so it delivers a user-centric, domain-relevant response. Core Prompt Engineering Techniques: Zero-Shot Prompting: Giving the model a clear instruction without providing any example of the desired output. What it is (Structure): - Instruction: A clear and specific task ("Do this..."). - Context: Any background information needed. - Constraints: How you want it done (tone, length, format) - Format: The shape or structure you expect the answer to take Example: "Summarize the following article in 3 bullet points, focusing on key findings and implications for healthcare" When to use it: Use this when the task is straightforward and doesn't need examples. The model can handle it with just clear instructions. Great for summaries, translations, or general knowledge tasks. Few-Shot Prompting: Providing a small number of input-output examples ("shots") to guide the model's behavior before asking it to handle the actual task. What it is (Structure): - Example 1: Input → Output - Example 2: Input → Output - ... - Actual Task: Input → ? Example: Tweet: "This product is amazing!" → Sentiment: Positive Tweet: "Terrible experience" → Sentiment: Negative Tweet: "It's ok, nothing special" → Sentiment: Neutral Tweet: "Best purchase I've ever made!" → Sentiment: ? When to use it: When the task involves following a pattern, specific formatting or when the model might misinterpret a zero shot prompt. Chain-of-Thought (CoT) Prompting: Asking the model to break its reasoning into step-by-step intermediate steps before ariving at a conclusion, enabling more accurate responses for complex tasks. What it is (Structure): - Problem: [some description] - Let's solve this step by step: 1) [First Step] 2) [Second Step] 3) [Conclusion] Example: A store has 15 apples. They sell 6 and receive 10 more. How many apples do they have? 1) "Start with 15 apples" 2) "Sell 6: 15 - 6 = 9 apples" 3) "Receive 10 more: 9 + 10 = 19 apples Answer: 19 apples When to use it: When tasks are more complex - multi-step reasoning, math problems or things that require logical chains. Role-Based Prompting: Instructing the model to adopt a specific persona or professional role (e.g., "You are a senior data scientist") in order to shape tone, style, and domain relevance. What it is (Structure): "You are a [specific role] with [expertise] Your task is to [specific action] for [target audience]. Consider [important factors] Example: You are a senior software architect with 15 years of experience in distributed systems. Explain microservices architecture to a team of junior developers, using practical examples and highlighting common pitfalls. When to use it: When you want the output to read like it's coming from a subject-matter expert, tailored to a specific audience or domain. Template-Based Prompting: Using a structured prompt format with placeholders (TASK, CONTEXT, INPUT, OUTPUT FORMAT) to ensure consistency, reuse, and clarity across prompt variants. What it is (Structure): TASK: [Task type] CONTEXT: [Background info] INPUT: [Specific input data] CONSTRAINT: [Requirements/limits] OUTPUT FORMAT: [Expected structure] Example: TASK: Email generation CONSTRAINT: Customer complaint about delayed delivery INPUT: Order #12345, delayed by 3 days CONSTRAINT: Professional, emphatic, under 150 words OUTPUT FORMAT: Subject line + email body When to use it: When the task is repeated, part of a workflow, or needs consistent format across prompts - great for teams, scale, and reuse. Why Prompt Engineering is Significant: Cost Efficiency: Training a new language model from scratch can cost $millions in compute. By using prompt engineering, you skip that: you work with an existing model and simply guide it with clever inputs. That means you can test, refine, and iterate in minutes rather than weeks, reuse the same prompt across many requests, and save major resources while still scaling. Immediate Impact: A well-crafted prompt can dramatically improve the accuracy because you are not changing the model, changes happen instantly: tweak the prompt and the output shifts right away. This enables rapid experimentation and fast prototyping of ideas. Accessibility: You don't need a PhD in machine learning to make this work. Prompt engineering means anyone who understands the task and how to ask clearly can get results. It democratizes AI - turning powerful models into tools usable by people across business. Business Value: From a business perspective, better prompts translate into better user experiences. When your AI gives accurate, relevant and consistent responses, users trust it and adopt it. That means higher engagement, stronger competitive advantage and more return on your investment. Examples: ❌ Poor Prompt: "Write about AI" ✅ Better Prompt: "Write about AI applications" ✅ Good Prompt: "You are a technical writer. Write a 200-word article about AI for business audience, focusing on practical applications and benefits." ✅ Excellent Prompt: "You are a senior technical writer specializing in AI for business audiences. TASK: Write an engaging article about AI applications in finance. AUDIENCE: Business executives with limited technical knowledge REQUIREMENTS: Length: 250-300 words Tone: Professional yet accessible Focus: Practical applications with ROI examples Structure: Introduction, 3 key applications, conclusion Include: Specific examples from successful implementations CONSTRAINTS: Avoid technical jargon Emphasize business value Include actionable insights

**source: GeeksforGeeks (image) Understanding Large Language Models (LLMs) Large Language Models (LLM) are powerful neural networks, typically comprising billions of parameters, that are trained on huge volumes of text so that they can understand, generate, and reason about human-language content. They mark a major shift on how natural-language processing (NLP) systems are built and deployed. Key Characteristics Scale: LLMs often run into billions of parameters. For example, some well-known models push into the 100s of billions (or even beyond) Training data: They draw on massive text corpora - web pages, books, articles - enabling rich language understanding. Architecture: Most are based on the Transformer model (self-attention, multi-head attention, etc.). Capabilities: Thanks to their scale + training, LLMs can do things like text-generation, summarization, language translation, question-answering, and even some forms of reasoning. How LLMs Work - A simplified View Pre-training: In the pre-training phase, a LLM is given a very large volumes of text data (books, articles, web pages, etc.). The idea is that the model learns the general patterns of language - grammar, syntax, semantics, factual knowledge, how words relate to one another in context - without being told a specific task. Fine Tuning: After pre-training, the model is adapted (or refined) for a specific task, domain or business use-case. This is the fine-tuning stage. Inference: Once the model is trained (pre-trained + fine-tuned), you run it in production or for use cases. This is inference: feeding a prompt or input, and the model generates output (text) based on its learned language patterns. Practical Analogy Think of an LLM like a "super-reader" who has read millions of books and articles, and now can: Autocomplete sentences Answer questions (like a knowledgeable conversation partner) Write stories or articles (creative writing assistant) Translate between languages Architecture Flow Input Text → The raw human-readable sentence or prompt that you feed into the model. Tokenization → The process of splitting the input into smaller units (tokens) and mapping them to numeric identifiers. Embeddings → Each token ID is converted into a continuous vector representation that captures semantic meaning. Transformer Layers → A stack of self-attention and feed-forward layers that process embeddings to build contextual language understanding. Output Probabilities → The model computes a probability distribution over the vocabulary for what the next token (or sequence of tokens) should be. Generated Text → The selected tokens are mapped back to human-readable text, producing the model's final output. Computational Requirements for Training an LLM? Training an LLM is non-trivial in terms of infrasturcture, cost, time, and engineering complexity. Below are the key dimensions. Hardware & Infrastructure To train large models you need massive compute resources , many GPUs, high-end memory, fast interconnect, storage for datasets, etc. Hardware must support efficient parallel and distributed training, model parallelism, data parallelism, memory optimisation techniques are used for training models with hundreds of billions of parameters. The memory footprint grows with model size: storing weights, activations, gradients, optimizer states all require substantial memory Compute / Training Steps Training cost often correlates with number of parameters × number of tokens (data) × compute per token . Some scaling-law research shows that for optimal performance you need to scale both model size and training data in tandem. Training the largest models can take weeks to months  of wall-clock time—even with state-of-the-art hardware. Data Requirements Huge volumes of text-tokens are necessary. As models’ parameter counts rise, so too must the number of training tokens to avoid “under-training”. The quality and diversity of training data matter: more domain-diverse, high-quality corpora help the model generalise better. Cost, Energy & Operational Considerations Training large LLMs is costly  — both in terms of direct compute infrastructure, energy consumption, and engineering overhead It also has an energy footprint: large GPU clusters consume large amounts of power and cooling Final Thoughts LLMs are one of the most exciting developments in AI and language processing in recent years. They combine scale , data , and architecture  in a way that unlocks powerful new capabilities. But with power comes complexity: the computational requirements for training them are substantial, which means practical adoption often involves fine-tuning rather than ground-up training. Saurabh Kamal https://www.linkedin.com/in/saurabh-kamal/

If you are working in the space of large language models (LLMs) and agent-based workflows, you've likely encountered both LangChain and LangGraph. They come from the same ecosystem, but they have different design goals, abstractions (level and style of conceptual modeling), and use cases. What is LangChain? LangChain is described as a framework for developing applications powered by large language models (LLMs). It emphasizes chains of components (like model calls, prompt processing, document retrieval, and tool invocation) that you can string together to build an LLM-driven application. "Chains of components" = Ready-made AI steps you string together in order, like LEGO, to make something smart. Model calls -> "Ask the AI a question" Prompt processing -> "Write the question nicely" Document retrieval -> "Go find info in a file" Tool invocation -> "Use a calculator or search the web" So, LangChain lines them up in order to create a smart app, just like following a recipe. What problems does it address? LLMs on their own generate text-based answers. But real applications often need: access to external data (databases, documents, APIs), chains of actions (retrieve -> process -> summarize -> answer), memory, and tools. LangChain helps simplify that. Speed of iteration: because you can reuse building blocks (retrieval, tools, prompt templates) instead of reinventing for each new app. In other words, you don't have to build a new robot from scratch every time. Just grab the same arms, wheels, and brain you used before - snap them in - and boom, new robot in minutes. Typical use cases Retrieval-augmented generation (RAG) workflows: fetching documents, embeddings, passing to LLM for summarization or Q&A. Chatbots or assistants that need tool use (like calculating something, fetching data, calling an API) via the "agent" paradigm in LangChain. Rapid prototyping of LLM applications where you need existing building blocks for memory, chains, and tools. In other words, if you want to build a smart robot helper super fast? LangChain gives you a box of ready-made parts - like a memory chip, a tool belt, and instruction cards - so you just snap them together and go!" **Source: LangChain Ecosystem What is LangGraph? LangGraph is described as a framework for building, managing, and deploying complex AI agent workflows built on large language models (LLMs). Unlike simpler chain-based frameworks, LangGraph allows you to define workflows as graphs (nodes + edges) rather than strictly linear sequences. enabling more dynamic cyclic behavior. Essentially, where many LLM-driven apps follow a direct step A -> B -> C, LangGraph allows branching, loops, multi-agent collaboration, and human-in-the-loop interventions, making it suitable for long-running, adaptable workflows.   LangChain vs LangGraph Core Capabilites: Graph-based modelling: You build workflows as a network of nodes linked by arrows, and you can have loops and decision-points instead of just a straight line. Stateful execution: LangGraph keeps and updates a shared "state" across the workflow, allowing it to remember past interactions and work over long sessions. Human-in-the-loop & observability: You can pause the workflow for human review or intervention, and you get the tools to monitor and debug how agents are behaving. LangGraph places a strong focus on flexibility, control, and durability. Instead of using a high-level "Chain" of steps, you are given low-level building blocks so you can design workflows that adapt, branch out, persist, and involve multiple actors. It's specially designed for applications that involve many steps, ongoing memory, loops or multiple interacting agents and need oversight. Typical use cases Complex agent systems: multiple actors or agents interacting (one agent queries data, another writes output, and another human checks). Long-running workflows that require context retention over hours, and days. Scenarios where you need full control over the orchestrating of LLM steps, branching logic, and stateful memory across sessions. Enterprise workflows with human-in-the-loop, audit trail, and multi-stage actions. **source: Architecture: Multi-Agent Workflows with LangGraph LangChain vs LangGraph

Imagine you could assemble a team of intelligent assistants, each with a unique role, goal, and expertise, and they could collaborate to solve complex tasks automatically. That's exactly what CrewAI enables. In simple terms, CrewAI is a framework (a toolkit) for building teams of AI agents, not just one AI working alone, but many agents working together, each playing a specific role. According to its makers, it is a "lean, lightning-fast Python framework built entirely from scratch, enabling you to create AI teams where each agent has specific roles, tools, and goals." Here are the main ideas behind it: Agents: Think of this as individual AI workers. Each agent has a role (what they do), a goal (what they aim to achieve), and often a back-story or persona (which guides them how to think). Tasks: These are the pieces of work the agents do. Each agent picks up tasks suited to its role.  Crew: A group of agents working together - combining their roles and tools to accomplish a bigger goal. Tools & integrations: Agents use external tools (web searches, APIs, document retrieval, etc.) to fulfill their tasks. Workflow orchestration: There is coordination and sequencing - who acts when, what data flows between agents, and how the outcome is built step-by-step. In essence, CrewAI lets organizations move from "one big static AI model" to "many specialized AI collaborators" working together. What is CrewAI used for? CrewAI is useful whenever you have tasks that are: multi-step (not just "answer a question") require different expertise (research, summarization, fact-checking, decision-making), integrate with multiple tools/systems For example: Automating a customer support process where one agent reads the ticket, another searches the knowledge base, another drafts a response, and yet another verifies compliance. Building a content generation pipeline: one agent ideates a topic, another agent researches sources, another drafts the article, another reviews it, and another publishes it. A workflow for data analysis: one agent pulls data, another cleans it, another summarises it, another generates charts, and another contextualises it for executives. How useful is it? Pros (What makes CrewAI useful): ✔️ Each agent has defined responsibilities that make it easier to reason about what's happening and debug. ✔️ Agents can leverage external APIs/tools, which means they are not just "chatbots" but can act and execute. ✔️ Agent teams bring flexibility and modularity - easier to maintain, extend and specialise. ✔️ It supports structured workflows (Crews + Flows) for real-world automation rather than simple one-step prompts. Cons (What to watch out for): ⚠️ Since you would be managing many agents and workflows rather than a single prompt, it requires more architecture and setup (roles, tasks, monitoring). ⚠️ Using multiple agents + external tools + orchestrating flows may consume more compute and maintenance overhead. ⚠️ The system is only as good as the workflow you define - poor task definitions or agent roles may lead to broken or looping logic. Use Cases Customer Support & Advisory in Fintech Agent 1: Understands customer query ("I want to invest in ESG funds"). Agent 2: Searches internal knowledge base + external market data. Agent 3: Draft response, propose fund options. Agent 4: Checks compliance (e.g., suitability, KYC) before sending to customer. Outcome: faster, smarter advisory at scale.   2.   Automated KYC/AML Screening Agent 1: Collects customer data. Agent 2: Searches databases/watchlists. Agent 3: Generates a risk assessment summary. Agent 4: Verifies compliance rules and flags necessities. Advantages: speed, audit-traceability, consistency.   3.   Stock & Equity Research Analyst Agent Agent 1: "Data Collector": Gathers raw publicly available data (e.g. SEC filings, earnings reports, news headlines, market data) Agent 2: "Metric Synthesizer:" Takes the raw data and computes key financial metrics (growth rates, ratios, trend lines), extracts relevant bullet points. Agent 3: "Narrative Writer": Crafts a readable narrative or investment memorandum based on synthesized metrics and recent news - A polished summary report. Agent 4: "Bias/Check Agent": Reviews the narrative for bias, missing context or errors, checks for consistency with source data. In short, CrewAI offers a powerful shift from "single-AI assistant" to "team of AI agents working together."

In recent years,   large language models (LLMs)   like GPT, Claude, and Gemini have become incredibly powerful at generating human-like text. But they still have a problem: they rely only on what they were trained on. This means they can   hallucinate answers , give outdated information, or struggle with highly specialized topics. That’s where   Retrieval-Augmented Generation (RAG)   comes in. What is Retrieval-Augmented Generation (RAG)? RAG is a hybrid approach that combines two ideas: Retrieval:-  fetching relevant information from an external knowledge source (like a database or document store). Generation:-  using a language model to craft natural, coherent responses. Instead of depending only on the model’s memory, RAG allows the system to   look up fresh, domain-specific information in real time   and then use that context to generate answers. Why this matters: ✅ Fewer hallucinations ✅ More up-to-date responses ✅ Better handling of domain-specific tasks (finance, law, research, etc.) ✅ Transparent answers (you can even show users the documents that were used) How it works (in simple steps): (a) User asks a question. (b)   Retriever searches documents   using semantic similarity (via vector embeddings) (c) Relevant text chunks are pulled out (d)   LLM generates the final answer   using both its own knowledge and the retrieved context RAG vs Closed-Book LLMs: Real-World Application of RAG Here are a few ways RAG is already being used: Ask Questions Over PDFs Upload a research paper, policy document, or manual Break it into chunks → embed into vectors → retrieve → answer 2. Internal Document Assistants HR or IT bots that answer employee queries using internal policies or wikis 3. Smarter Search Bots Go beyond keyword search with semantic retrieval Perfect for customer portals, academic sites, or product knowledge bases 4. Multi-Agent Systems In AI agents, RAG works as a “memory provider” Example: A travel planner agent that retrieves flight and hotel data before generating recommendations The RAG System Architecture Think of RAG as a pipeline: RAG Architecture 1. Input Layer   — user’s question (plus optional metadata like role or timestamp)   2. Text Splitter/Chunker   — breaks big documents into smaller, meaningful chunks   3. Embedding Generator   — converts chunks into numerical vectors (embeddings)   4. Vector Store (Retriever)   — database that can find “most similar” chunks (e.g., FAISS, Pinecone, ChromaDB)   5. LLM Generator   — the actual model (GPT, Gemini, Cohere, etc.) that reads the retrieved chunks + question   6. Output Layer   — the final response, often with references to the source docs Tools for Building RAG Systems 1. LangChain   — A popular framework for chaining LLMs with tools, memory, and retrieval. It simplifies document loading, splitting, embeddings, and complete RAG workflows with support for agents and prompt templates. 2. LlamaIndex   — Designed for knowledge ingestion and retrieval with a node-based architecture for structured documents. Works seamlessly with LangChain and many open-source models. 3. OpenAI API   — Provides high-quality embeddings (text-embedding-3-small) and LLMs (gpt-3.5, gpt-4). Easy to integrate with RAG pipelines using LangChain, Pinecone, or ChromaDB. 4. Pinecone   — A fully managed vector database optimized for speed and scale. Ideal for handling large document sets where low-latency similarity search is critical. 5. ChromaDB   — A lightweight, open-source vector database. Perfect for small-to-medium RAG projects or local experimentation. 6. FAISS (Facebook AI Similarity Search)   — A fast, research-grade library for similarity search in embeddings. Best for local or academic setups, and can be combined with LangChain or used standalone. By combining   retrieval   with   generation , RAG systems reduce hallucinations, stay up to date, and adapt easily to new domains — without retraining the model itself. If you’re building chatbots, assistants, or research tools, RAG is the backbone you’ll want to explore.

Imagine a language model like a smart student who has read almost every book in the world. This student knows a lot about general knowledge and language, but sometimes isn’t perfect for a specific task you want them to do. Fine-tuning   is like giving this student   extra lessons on a specific topic : The student already knows general stuff (like grammar, facts, and reasoning). You now give them a smaller set of examples focused on your task (e.g., answering customer service questions, writing medical reports, or coding help). After learning from these examples, the student gets better at your specific task without forgetting everything they already know. So, in short:   Fine-tuning = teaching a pre-trained model extra lessons on a special topic so it performs better for that task. Fine-tuning Process: Start with Pre-trained Models: Use a model trained on general text. Modify the model for the specific task Use labeled data for the specific task Adjust parameters: Update model weights for the task. Types of Fine-tuning: Full Fine-tuning PEFT (Parameter Efficient Fine-tuning): (a). LoRA (b). QLoRA DPO (Direct Preference Optimization) **source: QLoRA Efficient Finetuning of Quantized LLMs Full Fine-Tuning: Full fine-tuning means updating all the knowledge inside the model to make it perform better on my specific task. Remember our model is like a student who has read the whole library. Full fine-tuning = you re-teach the student using your special lessons, and they adjust everything they know based on the new lessons. How it works: Start with a pre-trained model (already very knowledgeable). Prepare a task-specific dataset. Train the model on this dataset, and update all its internal parameters. After training, the model is fully adapted to my task. This is different from the LoRA/adapter methods, where only some parts of the model are updated. Pros of Full Fine-Tuning: Models becomes highly specialized for your task. Can achieve maximum accuracy because all parts of the model are adjusted. Cons of Full Fine-Tuning: Expensive: Requires a lot of computing power and memory. Time-Consuming: Training takes longer. PEFT (Parameter Efficient Fine-tuning): These are the methods where only a subset of parameters are trained; the bulk of the model’s weights remain frozen.   (a). LoRA (Low-Rank Adaption):  Imagine you have a very large language model (lots of parameters). Fine-tuning everything can be extremely expensive. LoRA says: instead of changing all those big weight matrices, keep the original weights frozen and   add   a small “adjustment” matrix that has a low-rank. So you’re learning a modest “delta” over the big model, rather than rewriting the whole thing. How it works: The pretrained weight matrix is frozen (unchanged). You introduce two small matrices A and B (with rank r much smaller than the full dimension) such that the model’s effective weight becomes  W′=W+α⋅(A×B) You only train A and B (i.e., the “delta”), not all of W. This drastically reduces how many parameters you must update and store. Limitations/trade-offs: Choosing the right rank (r) and scaling matters: too small → underfits; too large → you lose the efficiency benefits. Because you only allow a small “change budget” (low rank), you might not be able to adapt fully if your new task is very different or demands large shifts in behaviour. (b).   QLoRA (Quantized LoRA):  QLoRA builds on LoRA, adding another trick: quantization of the base model weights to very low precision (e.g., 4-bit) so that you can fine-tune   huge   models on smaller hardware. In other words: “we’ll freeze the big model, make it small/memory-light via quantization, and then inject a LoRA delta to adapt it”. How it works: Take the pre-trained model and quantize (compress) its weights to a low bit-width format (e.g., 4-bit “NormalFloat 4” (NF4)). This shrinks memory usage. Freeze the quantized weights (you won’t update them). Add low-rank adapters (LoRA) as before, and train those on your downstream task. Because the base is compressed, you can fine-tune very large models (e.g., 65 B parameters) on hardware that would otherwise be impossible. Advantages: Training very large models becomes feasible in more modest settings (less GPU memory required) You still get much of the performance benefit of fine-tuning a huge model. Combines “small update cost” (LoRA) + “small base memory cost” (quantization).   Disadvantages: There may still be a slight performance drop compared to full-precision fine-tuning (though in practice QLoRA claims near parity). Complexity is higher DPO (Direct Preference Optimization): When we want a language model to behave in a way humans like (e.g., being helpful, safe, etc.), one way is to collect   preference data : “Given prompt X, response A is preferred by a human over response B.” *source:   Directly Preference Optimizations: Your Language Model is Secretly a Reward Model Imagine you’re training a chatbot.   For each question (prompt), humans tell you: “Answer A sounds better than Answer B.” You collect lots of these “A is better than B” pairs. Now, you want your model to start preferring A-like answers over B-like ones.   In old methods (like   Reinforcement Learning with Human Feedback, RLHF ): You first train a  reward model  that scores how good an answer is. Then you train your chatbot (using reinforcement learning) to maximize that reward. That’s a   two-step   and sometimes   unstable   process.   DPO skips all that. Advantages: Often more stable and efficient. Good empirical results in aligning large language models with human preferences Limitations: Still need quality preference data: good prompts + well-judged pairs. It’s not  magic  — if your preferred/rejected pairs are noisy or biased, you’ll get poor alignment .

A/B Test for Onboarding Flow Optimization A seamless onboarding experience is a vital element in ensuring users stay engaged and eventually become loyal customers. For a neo bank, an e-commerce, or edtech company aiming to expand its user base, optimizing the onboarding process was a strategic priority. The onboarding flow is the first significant interaction users have with the application, and improving it could lead to higher user engagement, better retention rates, and ultimately, increased customer lifetime value (CLV). Our goal was to assess the impact of a newly designed onboarding flow compared to the existing one through a carefully structured A/B test. The results would provide actionable insights to refine user acquisition and experience strategies for the bank. Objective: The project aimed to: Evaluate whether the optimized onboarding flow improved user engagement and retention metrics. Provide robust, statistically significant insights to inform decision-making. The Experiment: We divided users into two groups: Control Group (A):  Users experiencing the existing onboarding flow. Test Group (B):  Users introduced to the new, optimized onboarding flow. The impact was measured across three critical metrics: Onboarding Completion Rate:  Percentage of users completing the onboarding process. Retention Rates:  Percentage of users active on Day 7 and Day 30 post-signup. Sessions Within the First Week:  Average number of sessions per user in their first week, representing initial engagement. Methodology: Phase 1: A/B Test Design Random Sampling: Users were randomly selected and split into control and test groups to eliminate bias. Sample Size Calculation: A statistical formula determined the number of users required to detect meaningful differences between the groups. This ensured adequate power for the test. Hypothesis Formulation: 3(a). Onboarding Completion Rate: Null Hypothesis (H₀): No difference exists between the groups for a given metric. Alternate Hypothesis (H₁): A significant difference exists, favoring the test group. 3(b). Day 7 Retention Rate: Null Hypothesis (H₀): No difference in Day 7 retention rate between the control group and the test group. Alternate Hypothesis (H₁): The Day 7 retention rate is different between the control group and the test group. 3(c). Day 30 Retention Rate: Null Hypothesis (H₀): No difference in in Day 30 retention rate between the control and test groups. Alternate Hypothesis (H₁): The Day 30 retention rate is different between the control group and the test group.   3(d). Sessions within a week: Null Hypothesis (H₀): A significant difference in session counts. Alternate Hypothesis (H₁): No significant difference in session counts. Data Preparation: The dataset was cleaned and prepped, ensuring the removal of duplicates and handling of missing values. Phase 2: A/B Test Execution Group Assignment: Users were evenly split into Control (A) and Test (B) groups. Statistical Testing: Onboarding Completion Rate and Retention Rates:  These binary metrics were analyzed using the Chi-Square Test. Sessions Within the Week:  This count data metric was analyzed using the Mann-Whitney U Test, a non-parametric alternative to the t-test. Decision Rules: Based on a p-value threshold (α = 0.05), the results were classified as statistically significant or not. Phase 3: A/B Test Evaluation Visualization: Tools like Power BI, Matplotlib, and Seaborn were used to create intuitive visualizations, aiding in the interpretation of results. Robustness Checks: Effect Size Analysis:  Quantified the magnitude of differences beyond statistical significance. Bootstrap Sampling:  Repeated resampling of the data provided confidence intervals for key metrics, ensuring the reliability of findings. Key Insights: Onboarding Completion Rate: The completion rates were similar for both groups, suggesting that the new onboarding flow did not significantly outperform the existing one. Day 7 Retention: The test group exhibited a noticeable increase in retention, indicating early engagement benefits from the new onboarding design. Day 30 Retention: Retention rates were similar across both groups, indicating that the initial engagement boost from the test group did not sustain over time. Sessions Within the First Week: Users in the test group completed more sessions, highlighting improved short-term engagement. Recommendations: Based on the findings, we proposed several strategies for further optimization: Personalized Onboarding: Tailor the onboarding experience for different user segments (e.g., new vs. returning users). Gamification Elements: Introduce progress bars, achievements, or rewards to motivate users. Streamlined Design: Reduce cognitive load by presenting essential features step-by-step. Qualitative Feedback: Gather insights from users who drop off during onboarding to identify pain points. Iterative Testing: Design follow-up A/B tests to explore specific hypotheses, such as the effect of gamification or personalized flows. Time Analysis: Track time spent on each onboarding step to identify bottlenecks. Conclusion The new onboarding flow demonstrated measurable improvements in early user engagement, particularly in Day 7 retention and weekly sessions. However, the lack of sustained impact on Day 30 retention suggests room for further refinement. By implementing our recommendations, the neo bank, an ecommerce, or an edtech company can enhance the onboarding experience, fostering higher user engagement and retention rates. This approach not only boosts customer satisfaction but also contributes to long-term growth and profitability.

Analyze competitor ad spend, placements, and messaging to uncover strategic insights. Gain a competitive edge and optimize your advertising for maximum impact. 1. How Can Brands Maximize the Impact of Their Advertising Strategies? To maximize advertising impact, brands need a data-driven approach  that evaluates performance across multiple channels. Market Mix Modeling (MMM):  This advanced analytical technique helps identify the true impact of each marketing channel, enabling brands to: Allocate budgets efficiently. Refine targeting strategies. Enhance messaging for higher engagement. By analyzing ad spend , placement trends , and competitor strategies , our solutions provide actionable insights that: Boost ROI. Drive conversions. Ensure sustained growth. 2. How Can Fintech Companies Unlock Maximum Growth? Market Mix Modeling (MMM)  provides a holistic view of marketing performance by analyzing historical data to determine the impact of various advertising channels on sales and conversions. Key Benefits for Fintech: Understand where, when, and how much  competitors are spending. Identify high-value sales prospects . Sharpen focus on the most effective advertising channels. Allocate budgets efficiently for maximum impact. With our data-driven solutions, fintech companies can confidently make smarter decisions to fuel growth and drive meaningful customer engagement. 3. How Can We Deliver the Best Results for Our Clients? Success in advertising comes from: Data-driven decisions Competitive intelligence Strategic execution Our Advertising Intelligence  solutions provide: Deep insights  into market trends, competitor ad spend, and audience behavior. Real-time data  and predictive analytics  powered by Market Mix Modeling (MMM) . Smarter investments  that: Improve campaign performance. Optimize budgets. Achieve measurable growth. Whether you’re looking to refine your messaging, enhance targeting, or boost ROI, our tailored solutions ensure maximum advertising efficiency. 4. How Can Brands Benchmark Their Creative Performance? Understanding how your creative stacks up against competitors is crucial for maximizing advertising impact. Our Advertising Intelligence Solutions Provide: Deep insights into ad performance across multiple channels. Analysis of ad spend patterns  to inform strategic decisions. Data-driven benchmarking tools to: Enhance visual appeal. Optimize campaign performance. With actionable insights, brands can gain a competitive edge by ensuring their ads stand out and resonate with their audience. Empower your campaigns with data-driven strategies for impactful results. Conclusion: Advertising Intelligence combines data, analytics, and strategic insights to help brands thrive in competitive markets. Whether you’re a fintech company, an emerging startup, or an established brand, leveraging these solutions ensures your advertising drives growth, captures audience attention, and delivers measurable results.

1. Identifying High-Potential Customer Segments Fintech companies can leverage customer data to divide their audience into distinct groups based on behavioral, demographic, and transactional patterns. Key approaches include: Behavioral Segmentation : Grouping customers based on their spending habits, transaction frequency, and financial product preferences. Demographic Segmentation : Identifying patterns based on age, income, location, or profession to tailor offerings. RFM Analysis : Using Recency, Frequency, and Monetary value to determine which customers are the most active and valuable. Benefits : Focus marketing efforts on the most profitable customer groups. Personalize products and services to meet specific segment needs. 2. Attracting New Customers Fintech companies can attract new customers by utilizing predictive models and targeted marketing strategies: Customer Lifetime Value (CLV) : Predict the potential long-term value of new prospects and focus acquisition efforts on those with the highest value. Data-Driven Marketing : Analyze which channels (social media, paid ads, referrals) bring in high-value customers. Sentiment Analysis : Use customer feedback and social media sentiment to craft appealing campaigns. Referral Programs : Leverage existing satisfied customers to attract similar new customers. Benefits : Optimize acquisition costs by focusing on high-ROI channels. Deliver personalized onboarding and targeted offers to new customers. 3. Retaining Existing Customers Customer retention strategies rely on understanding and addressing the reasons behind customer churn: Churn Prediction Models : Analyze patterns such as reduced activity or dissatisfaction to identify customers at risk of leaving. Proactive Engagement : Offer personalized rewards, discounts, or better rates to at-risk customers. Enhanced Customer Experience : Use feedback and transaction data to identify and resolve pain points. Upselling and Cross-Selling : Recommend relevant financial products based on past behavior and preferences. Benefits : Reduce churn-related revenue losses. Build long-term customer loyalty and satisfaction. Tools and Techniques to Achieve These Goals Machine Learning Models : Customer Segmentation : K-Means Clustering, Hierarchical Clustering. Attracting New Customers (CLV Prediction) : Regression models like XGBoost, Random Forest, and Gradient Boosting; Deep Learning models such as LSTM and ANN. Churn Prediction : Classification models like Logistic Regression, Random Forest, XGBoost, and deep learning models such as LSTM or GRU. Customer Feedback Analysis : Natural Language Processing (NLP)  techniques like Sentiment Analysis using tools such as VADER, BERT, and RoBERTa to assess customer feedback and reviews. Conclusion: By leveraging customer segmentation, CLV prediction, and churn analysis, fintech companies can create focused strategies to identify high-value customer groups, attract new customers, and retain existing ones. Combining advanced machine learning models, big data platforms, and personalized marketing ensures scalability, customer satisfaction, and business growth.

Customer Sentiment Analysis plays a crucial role in understanding user feedback, improving services, and enhancing customer satisfaction. The architecture depicted in the diagram provides a systematic approach to collecting, processing, analyzing, and visualizing customer sentiments using machine learning models  and natural language processing (NLP) techniques . The entire process is divided into multiple stages, from data collection  to final insights generation , ensuring that organizations can make data-driven decisions. 1. Data Collection from Multiple Sources The sentiment analysis process begins with the collection of data from three key sources : Website : Customer satisfaction ratings and reviews that express sentiments about the platform. Surveys/Feedback : Study material and tutor rating responses that provide insights into learning effectiveness and tutor performance. Support Applications : After-support ratings and video quality assessments that help evaluate customer service interactions and content clarity. These sources generate a mix of structured (ratings) and unstructured (text feedback) data , forming the foundation for sentiment analysis. The data is stored in a centralized system  (depicted as a cloud database) that consolidates feedback from different sources for further processing. 2. Data Preprocessing Using Databricks & PySpark Since customer feedback is often noisy and unstructured, data preprocessing  is a crucial step to ensure high-quality and meaningful  input for analysis. The Databricks and PySpark framework is used to efficiently process large datasets with high-speed computations. The preprocessing steps include: Removing Noise : Eliminating unnecessary elements like HTML tags, special characters, and emojis that do not contribute to sentiment understanding. Handling Missing Data : Addressing missing values in customer reviews by either imputing meaningful values or removing incomplete records. Standardizing Text : Ensuring uniform formatting to prevent inconsistencies in language variations. Removing Stopwords : Common words like "the," "is," and "and" are removed since they do not provide contextual meaning. Removing Punctuation : Eliminating punctuation marks that can interfere with text tokenization and NLP processing. Converting to Lowercase : Standardizing all text to lowercase to prevent duplicate words due to casing differences. Stemming and Lemmatization : Reducing words to their root form (e.g., "running" → "run") to enhance text consistency and improve model accuracy. This preprocessing stage cleans and structures  the raw data, preparing it for sentiment classification. 3. Sentiment Labeling Using TextBlob Once the data is preprocessed, TextBlob , a powerful NLP library, is used for sentiment labeling . TextBlob assigns a sentiment polarity score  to customer feedback, categorizing them as: Positive Neutral Negative The result is a labeled dataset  containing both the cleaned text and its corresponding sentiment score. This labeled data serves as the ground truth  for training machine learning models. 4. Feature Engineering for Machine Learning Models To convert textual feedback into a format that machine learning models can understand, feature engineering is applied. The architecture incorporates multiple feature extraction techniques , including: TF-IDF (Term Frequency-Inverse Document Frequency) : Assigns weight to words based on their importance in a document relative to the entire dataset. Word2Vec : Converts words into numerical vectors based on their context in the sentence. Bag of Words (BoW) : Represents text as a frequency matrix, counting word occurrences in each review. HFE (Hybrid Feature Extraction) : A combination of different techniques to enhance feature representation. These transformations convert the raw text data into numerical representations , making it suitable for machine learning models. 5. Topic Extraction Using Latent Dirichlet Allocation (LDA) Beyond sentiment classification, it is also essential to understand common themes in customer feedback . LDA (Latent Dirichlet Allocation)  is used for topic extraction , identifying underlying subjects in text reviews. For example, it may reveal topics like: Tutor Quality Course Content Issues Technical Support Problems Topic modeling helps businesses prioritize areas for improvement  based on frequently discussed concerns. 6. Machine Learning Models for Sentiment Classification After feature extraction, the dataset is divided into training and testing sets , and multiple machine learning models are trained to classify sentiments. The architecture utilizes four key models: Naive Bayes : A probabilistic classifier commonly used for text classification. Random Forest : An ensemble learning algorithm that combines multiple decision trees for robust predictions. Logistic Regression : A statistical model well-suited for binary and multi-class classification tasks. XGBoost : A gradient boosting algorithm that improves model accuracy and reduces overfitting. Each model is trained on historical sentiment-labeled data  and evaluated to determine the best-performing classifier. 7. Model Evaluation Metrics To assess model performance, standard evaluation metrics  are used: Precision : Measures the proportion of correctly predicted positive sentiments. Recall : Assesses how well the model captures actual positive sentiments. F1 Score : The harmonic mean of precision and recall, balancing both measures. Accuracy : Overall correctness of sentiment classification. For better performance, BERT (Bidirectional Encoder Representations from Transformers)  is used as an advanced NLP model . BERT significantly improves context understanding  and sentiment prediction accuracy  by analyzing words in relation to their surrounding text. 8. Visualization & Actionable Insights After sentiment classification, the insights are visualized using interactive dashboards , providing decision-makers with a clear understanding of customer sentiment trends . The visualizations include: Actionable Recommendations Enhancing tutor training based on sentiment trends. Improving study material and support services. Key Performance Indicator (KPI) : Student Retention Rate (%) . Complaint Heatmap Identifies the top complaint categories . Helps prioritize course improvements . Best vs Worst Rated Courses Ranks courses based on sentiment and reviews. KPI : Course Satisfaction Index . Sentiment Trends Over Time Tracks customer satisfaction changes  over months. KPI : Monthly Sentiment Change (%) . These insights help businesses optimize learning experiences, improve customer service, and enhance product offerings .   The chart presents the sentiment trends  of customer reviews over time, categorized into Positive (Red), Negative (Blue), and Neutral (Gray)  sentiments. The x-axis represents the months from January 2023 to January 2024 , while the y-axis indicates the number of reviews  in each sentiment category. Key Observations: Positive Sentiments Dominated Throughout the Year The positive sentiment (red line) consistently remained the highest among all three categories. There were slight fluctuations, but overall, the number of positive reviews remained above 1000 reviews per month . Peaks can be observed at certain intervals, indicating periods of higher satisfaction and engagement . Negative Sentiment Trends (Blue Line) The negative sentiment  line remained stable, fluctuating around 600-700 reviews per month . It saw a minor increase in March and August , possibly indicating customer dissatisfaction spikes during these periods. However, the trend did not show significant improvement or worsening. Neutral Sentiment Trends (Gray Line) The neutral sentiment  remained the lowest throughout the year, maintaining an average around 400-500 reviews per month . The variations were minor, indicating a relatively stable segment of users who provided neutral feedback . Sudden Drop in All Sentiments in January 2024 A drastic decline in all sentiment categories is observed at the start of 2024 . This could indicate: A drop in customer engagement  (fewer reviews collected). Data collection issues  (e.g., missing or incomplete data). A major policy change or platform issue , reducing user feedback. End of a campaign or service cycle , leading to fewer interactions. Possible Business Insights & Recommendations: Monitor fluctuations in positive sentiment trends : Identify the causes behind peaks in satisfaction and replicate the successful strategies. Investigate reasons for occasional negative sentiment spikes : Perform a root cause analysis for March and August dips  to mitigate similar issues in the future. Address the drastic decline in January 2024 : Validate if this is a data anomaly  or a genuine decline in customer engagement . Enhance Customer Support & Engagement Strategies : Since negative feedback remains consistent, targeted improvements in customer service, product features, and issue resolution  may help reduce negative reviews.   The horizontal bar chart represents the average sentiment scores  for various courses, categorizing them into best-rated (positive sentiment) and worst-rated (negative sentiment) . The x-axis shows the average sentiment score , with positive values indicating better sentiment and negative values indicating poorer sentiment . The y-axis lists different courses , with blue bars representing lower-rated courses and red bars representing higher-rated courses . Key Observations: Worst-Rated Courses (Negative Sentiment - Blue Bars) P3: Advanced Financial Management (AFM) has the lowest sentiment score , making it the worst-rated course. Other poorly rated courses include: Financial Management (FM) Taxation (TX) Performance Management (PM) Financial Accounting (FA) These courses have negative sentiment scores , indicating dissatisfaction among students. Possible reasons could be: Course difficulty and complexity. Poor study material or inadequate explanations. Low-quality tutor support or ineffective teaching methods. Moderately Rated Courses (Neutral Sentiment - Light Shades) Courses like Strategic Business Leader (SBL), Business and Technology (BT), and Advanced Audit and Assurance (AAA) are closer to the zero line , indicating mixed feedback  from students. These courses may need minor improvements in content delivery or teaching methods  to shift toward a more positive sentiment. Best-Rated Courses (Positive Sentiment - Red Bars) The highest-rated course is P4: Advanced Performance Management (APM) , with the most positive sentiment score . Other well-rated courses include: Audit and Assurance (AA) Advanced Taxation (ATX) Corporate and Business Law (LW) Strategic Business Reporting (SBR) The positive sentiment  indicates that students find these courses well-structured, easy to understand, and beneficial . These courses may have better study material, engaging instructors, and more effective support systems . Business Insights & Recommendations: Investigate the Issues in Poorly Rated Courses Conduct surveys or analyze student feedback to understand why P3: Advanced Financial Management (AFM) and Financial Management (FM)  received negative sentiment. Improve study materials, tutor support, and exam preparation guidance  for these courses. Consider introducing additional resources like webinars, mentorship sessions, or simplified content . Leverage Insights from Best-Rated Courses Identify what makes P4: Advanced Performance Management (APM) and other top courses successful . Replicate best practices  in content structure, tutoring methods, and learning support for other courses. Monitor Trends Over Time Continuously track sentiment changes to ensure improvements in poorly rated courses . Implement feedback-driven enhancements and observe if the sentiment scores improve over time.   Key Observations: Most Complained About Courses The courses with the highest number of complaints include: Performance Management (PM) – 598 complaints Taxation (TX) – 596 complaints F2: Management Accounting (MA) – 591 complaints Audit and Assurance (AA) – 588 complaints Financial Accounting (FA) – 588 complaints These courses are represented in the darkest shades , indicating significant dissatisfaction  among students. Courses with Moderate Complaints Courses such as: Corporate and Business Law (LW) – 543 complaints Strategic Business Leader (SBL) – 552 complaints Advanced Audit and Assurance (AAA) – 557 complaints These courses still receive a substantial number of complaints  but are not the worst . Least Complained About Course P3: Advanced Financial Management (AFM) – 499 complaints  received the fewest complaints. This course appears in the lightest shade , indicating relatively lower dissatisfaction  compared to other courses. Potential Reasons for Complaints: Course Complexity:  Courses like Performance Management (PM), Management Accounting (MA), and Taxation (TX)  are known for high difficulty levels , leading to more complaints. Lack of Study Resources:  Courses with high complaints may lack adequate learning materials, instructor support, or structured content . Exam Challenges:  If students find exams too difficult or unfairly graded , it could lead to increased dissatisfaction . Tutor Effectiveness:  Courses with ineffective tutors or poor teaching methodologies  could result in higher negative feedback . Business Insights & Recommendations: Investigate the Root Cause of Complaints in High-Risk Courses Conduct student surveys  to understand the primary reasons behind dissatisfaction in Performance Management (PM), Taxation (TX), and Management Accounting (MA) . Improve study material, exam preparation guidance, and tutor training . Enhance Learning Support for the Most Complained Courses Offer additional online tutorials, mentorship sessions, or Q&A forums . Provide simplified explanations or real-world examples  to aid understanding. Leverage Strengths from the Least Complained Course P3: Advanced Financial Management (AFM)  received the fewest complaints . Analyze what makes this course more effective and apply those practices  to other courses. Track Complaint Trends Over Time Continuously monitor if complaints decrease after implementing changes . If complaints persist, conduct further evaluations and feedback sessions . Conclusion: This analysis identifies high-risk courses based on complaint volume , helping institutions prioritize improvements . By addressing concerns in the most complained about courses , educators can enhance student satisfaction, improve course effectiveness, and reduce negative feedback over time .

With the growth of multimedia technologies and Machine Learning (ML), it is becoming easier for individuals to create fake images/videos. Generative Adversarial Network (GAN) models are mainly used to generate accurate deepfakes, and then the fake content is distributed as news via the World Wide Web. Researchers are rapidly aiming to develop tools to combat the spread of false news, a major global threat. Fake content on major social media sites has had, and can have, real-world ramifications on people’s opinions and actions. This may only be the start of a race to identify solid algorithms that can combat deceitful information. The primary goal of this study is to identify fake news and deepfakes by leveraging quantum machine learning, and then comparing the training time with a traditional neural network model. Link to -> https://cmte.ieee.org/futuredirections/tech-policy-ethics/july-2022/quantum-machine-learning-based-detection-of-fake-news-and-deep-fake-videos/

AI-driven holographic lessons in action Generative Artificial Intelligence (AI) is rapidly transforming the landscape of educational technology. By automating routine tasks, tailoring learning experiences to individual needs, and freeing up educators to focus on human-centered interaction, these powerful tools are unlocking new possibilities in classrooms, online courses, and corporate training programs. Below, I explore the key ways generative AI is reshaping teaching, learning, and administration, and what lies ahead as the technology matures. Personalized Learning at Scale One of the most compelling applications of generative AI in education is its ability to craft individualized learning paths. By analyzing each student’s performance data, quiz scores, time on task, error patterns, AI systems can generate: Adaptive Exercises:  Custom problem sets that target a learner’s specific skill gaps while challenging their strengths. Dynamic Explanations:  Multiple versions of an explanation or example, adjusted for reading level, background knowledge, or preferred learning style. Rather than one-size-fits-all worksheets or static lesson plans, teachers can deploy AI‑driven modules that evolve in real time with each student, ensuring no one is left behind and advanced learners remain engaged. Automating Content Creation Teachers and instructional designers spend countless hours crafting curriculum materials. Generative AI accelerates this process by: Drafting Lesson Plans:  From learning objectives to project ideas, AI can outline modules aligned with specific standards (e.g., Common Core, IB). Producing Rich Media:  Automatically generated diagrams, mock lab‑report data, or simple animations bring abstract concepts to life without requiring advanced design skills. By outsourcing the initial drafts and media creation to AI, educators can focus their expertise on refining content and delivering engaging in‑person or synchronous experiences. Enhancing Accessibility and Inclusivity Generative AI also plays a vital role in making education more equitable: Multilingual Translation:  Real‑time conversion of course materials and live lecture captions supports non‑native speakers in fully participating. Alternative Formats:  AI‑generated summaries, text‑to‑speech narration, and large‑print or high‑contrast documents help learners with disabilities access content in their preferred modality. By automatically tailoring materials for diverse needs, AI-driven platforms ensure that no student is marginalized by format or language barriers. Streamlined Assessment and Feedback Assessment is the linchpin of effective learning—but grading and feedback are time‑consuming: Instant Grading:  Generative models can score multiple‑choice, short‑answer, and advancing rapidly, even essay responses, providing students with immediate performance indicators. Targeted Feedback:  Beyond numeric scores, AI can deliver nuanced comments (“Your thesis statement is clear; consider adding evidence in paragraph three”) and link to supplementary resources for remediation. This continuous, AI‑powered feedback loop keeps learners informed about their progress and frees educators to offer deeper, higher‑order feedback where human judgment is irreplaceable. Challenges and Ethical Considerations As with any powerful technology, generative AI carries risks that demand vigilant oversight: Accuracy and “Hallucinations”:  AI models sometimes produce plausible-sounding but incorrect information. Every AI‑generated lesson, explanation, or assessment must be reviewed by a knowledgeable educator. Bias and Fairness:  Training data can reflect historical inequities. Institutions must implement bias‑detection audits and continuously refine models to prevent perpetuating stereotypes or disadvantaging any group. Data Privacy:  Personalized learning relies on collecting sensitive student data. Robust consent processes and compliance with regulations like GDPR are nonnegotiable. Teacher Empowerment vs. Replacement:  AI should amplify, not replace, the human relationships, empathy, and professional judgment at the heart of effective education. As these tools mature, the guiding principle must remain clear: generative AI is most powerful when it serves to elevate human creativity, compassion, and expertise, making education more adaptive, inclusive, and inspiring for all.