Preetam Joshi’s Post

🧠 RAG is leveling up �� the next generation is already here. Retrieval-Augmented Generation (RAG) started simple: retrieve → augment → generate. But in Part 2 of our series, we explore how RAG is getting smarter, self-learning, and more connected 🔁 Here’s what you’ll learn in this post 👇 ✅ Feedback RAG → Learns from past answers or user input to continuously improve retrievals. ✅ Auto RAG → Automates data refresh & embedding updates, keeping your knowledge base up-to-date. ✅ REALM (by Google) → Integrates retrieval directly into model training for deep factual reasoning. ✅ Graph RAG → Builds knowledge graphs that connect related ideas — enabling multi-hop reasoning. 💡 Key Takeaway: These advanced RAG types push AI from static knowledge to dynamic understanding. They don’t just recall facts — they adapt, learn, and reason like intelligent systems. 📌 Missed earlier posts? Catch up: 🔹 Part 1 – RAG Foundations → https://lnkd.in/dEKT8d99 🔹 Part 2 – Types of RAG (Overview) → https://lnkd.in/gq-AFhWs ✍ Content by Niket Girdhar 👥 Follow Peer Grid for simple, visual explainers on AI, LLMs, and modern tech systems. #PeerGrid #RAG #RetrievalAugmentedGeneration #AI #LLM #MachineLearning #GenAI #TechExplained #GraphRAG #REALM #AutoRAG

Day 172 of my AI Engineer Journey Today's implementation: I coded KNN from scratch and discovered why the simplest ML algorithm is also the most impractical for production. The process: Implemented distance calculations for every query point Tested K=1, K=5, K=50 on same dataset Visualized decision boundaries Watched prediction time explode on larger data What I learned: K=1: Overfitting nightmare (98% train, 65% test accuracy) K=5: Sweet spot (85% train, 83% test accuracy) K=50: Underfitting (70% train, 68% test accuracy) The brutal reality: Training time: 0.001 seconds Prediction time: 12 seconds (for 10,000 points!) Why KNN fails at scale: Every prediction calculates distances to ALL training points. 1 million training samples = 1 million distance calculations per prediction. Key insight: KNN is a "lazy learner" - does zero work during training, all work during prediction. Exactly opposite of what production needs. The curse of dimensionality: Added 50 features to dataset. KNN accuracy dropped from 85% to 62%. Distance becomes meaningless in high dimensions. Simple in theory ≠ practical in reality. Elegant code ≠ scalable solution. Question: Have you ever built something that worked perfectly in development but failed in production? #MachineLearning #KNN #ProductionML #RealWorldML #AI #LearningInPublic

Hybrid RAG System: Solving AI's Hallucination & Accuracy Problem Ever been frustrated by an AI that confidently gives you wrong or irrelevant answers? 🤯 We were too. That's the classic problem with standard RAG systems. They often rely on a single retrieval method, which leads to: ❌ Missing Context: Critical information gets left behind. ❌ Irrelevant Retrieval: The system fetches documents that don't actually answer your question. ❌ AI Hallucination: The LLM invents facts because it lacks the right source material. But what if you could combine the best of all retrieval worlds? We built a solution. ✨ Introducing our Hybrid RAG System with Intelligent Scoring! We engineered a system that doesn't just hope for the best result it intelligently finds and ranks it. 🔍 Our Hybrid Retrieval Approach: · Vector Search for deep semantic understanding. · Keyword Search for precise term matching. · Dense Passage Retrieval for high quality context. 📊 The Secret Sauce: Our Scoring Engine Each retrieved document is evaluated by a multi factor scoring model: · Semantic Similarity Score: How conceptually close is it to the query? · Relevance Score: Does it directly address the question? · Diversity Score: Are we getting a balanced perspective? · Confidence Score: A final, weighted score to select the best sources. 🎯 Proven by Hard Metrics: Our system isn't just theoretically better; the numbers prove it: · MRR (Mean Reciprocal Rank): 0.85 · NDCG@K (Normalized Discounted Cumulative Gain): 0.78 · Precision@5: 0.82 · Recall@10: 0.75 The result? A dramatic 40%+ boost in accuracy compared to a traditional, single-method RAG! 🚀 Now, the AI can reliably: ✅ Provide accurate, well grounded answers. ✅ Cite the most relevant source documents. ✅ Significantly reduce hallucinations. ✅ Handle complex, multi faceted questions with ease. Ready to see how it works and build your own? 🔗 Explore the full code on GitHub: https://lnkd.in/dA63mMhN Give the repo a ⭐ if you're as excited about building more reliable AI as we are! #HybridRAG #RAGSystem #AI #MachineLearning #NaturalLanguageProcessing #LLM #VectorSearch #AIEngineering #DataScience #OpenSource #TechInnovation #AIAccuracy

🤖📊 Machine Learning! Today I explored the K-Nearest Neighbors (KNN) algorithm — a simple yet effective method that makes predictions based on the closest data points in the feature space. KNN works like this: choose a number [k], measure distances from a new point to all training points, find the [k] closest neighbors, then predict by majority vote (classification) or average (regression). Why it’s useful: •Intuitive, non-parametric, and great for quick baselines. •Works for both classification and regression tasks. •Flexible with different distance metrics like Euclidean, Manhattan, or Cosine. Things to watch: •Sensitive to feature scale — standardize to avoid distorted distances. •Choice of [k] matters: small [k] can overfit; larger [k] can underfit. Tune via validation. •Can be slow on large datasets since it searches neighbors at query time. What fascinates me most is how KNN mirrors “similarity-based” reasoning: if it looks like these neighbors, it probably belongs with them — elegant and practical. Excited to apply KNN with scaling, metric tuning, and smarter [k] selection on real datasets next! 🌱🤖 #MachineLearning #KNN #AI #DataScience #LearningJourney

The RAG Revolution: Making LLMs Fact-Check Their Homework 🤓 If an LLM hallucinates in the forest, and no one is around to correct it, does it still make a sound? Not if you use RAG! 😉 This diagram is the key to creating smart, factual, and reliable Generative AI applications using Retrieval Augmented Generation (RAG). The process is simple, yet revolutionary: 1. Ingestion 🧱: We chop up large documents into small "chunks," turn them into numerical embeddings, and store them in a searchable database. This builds our custom knowledge base. 📚 2. Query & Retrieval 🔍: When a user asks a question, RAG acts as an intelligent librarian. It quickly finds the most relevant pieces of information from our database and feeds them directly to the LLM as context. 3. Generation ✅: The LLM, now armed with the exact facts it needs, generates a precise, grounded, and accurate answer instead of guessing. RAG is how we tame the power of LLMs, ensuring they speak with authority and your company's data. It’s a game-changer for enterprise knowledge management! 🚀 What's the toughest knowledge challenge you've solved using RAG? Share below! 👇 #RAG #LLM #GenerativeAI #AIArchitecture #VectorDatabases #DataScience #TechExplained #AICommunity #MachineLearning

I broke my AI agent 4-5 times before I figured this out 😅 I was building an AI agent to fetch the latest research papers and summarize them. Everything worked… until the 2nd run, when I realized my agent kept re-searching papers instead of remembering what it had already found. After some trial and error, I discovered the difference between two memory types: >>ConversationBufferMemory: Stores dialogue text sequentially. Great for keeping context in a conversation, but doesn’t remember structured data like past search results. >>VectorStoreRetrieverMemory: Stores embeddings of outputs, allowing the agent to recall previous results and reuse them in new queries. 💡 The fix: Save the search results in vector memory and feed them back to the agent when summarizing. Suddenly, my agent became context-aware and efficient—no more repeated searches! Takeaway: Struggling is the fastest way to mastery—if you reflect and learn from each failure. What’s the trickiest memory or context bug you’ve solved in an AI agent—and how did you fix it? #AIAgents #LangChain #RAG #VectorDB #LLM

Have you ever asked a research scientist to check for typos? One of the biggest lessons I’ve learned while working with LLMs is that not every problem needs the same amount of brainpower. In some of my earlier, unpublished personal projects, every task, big or small, went straight to the LLM for analysis. That meant powerful AI models were spending time on things they didn’t need to. It was like asking a research scientist to check spelling errors. The result? >Higher costs and slower responses< To fix this, introduce something called adaptive LLM gating. Here’s how it works, in simple terms: - The system first looks at how obvious or uncertain a decision is. - If the answer is clear, the system handles it automatically. - If the case is complex or borderline, it “opens the gate” and lets the LLM step in to reason more deeply. This means the model only engages when human-level judgment is actually useful. The outcome has been powerful: - Unnecessary LLM calls are reduced significantly, resulting in substantial cost savings. - The tool runs faster and more efficiently. - The AI feels smarter because it is programmed when to think hard, and when not to.   Adaptive gating is all about managing intelligence wisely. It’s one of the quiet shifts making modern AI systems more practical and scalable in the real world. #LLMs #ArtificialIntelligence #MachineLearning #GenerativeAI #AIDevelopment #NaturalLanguageProcessing #CostOptimization #TechInsights #FutureOfAI

Day 11 — Exploring and Learning AI : Deep Dive into RAG (Retrieval-Augmented Generation) Over the past two days, I’ve been diving into one of the most exciting frontiers in AI Retrieval-Augmented Generation (RAG) Here are some of my key takeaways + reflections: 🔸 About RAG RAG = Retrieval + Generation Instead of relying solely on a model’s pre-training knowledge, RAG lets the model “look up” relevant information (via a retriever) and then generate answers grounded in that data. 🔸 Why it matters Traditional LLMs face two big limitations: knowledge cutoff and hallucination (i.e. confidently making up incorrect facts). RAG helps overcome both by fetching up-to-date, domain-specific information as context before answering. I also wrote a detailed Medium article on it and kept things easy to understand with examples .. Link here : 🔗 https://lnkd.in/g3fHctvu

🧠 “Will RAG stay relevant, given recent advances in AI?” We get this question a lot, and the short answer is: yes, because RAG complements the tech driving those advances. But if you’re one of those people who likes longer explanations, keep reading! 🧠 The dramatic increase in size of context windows has led some people to wonder if RAG is still necessary for LLMs. After all, if an entire database can fit in an LLM’s context window, why would it need to consult an external database? At the risk of oversimplifying: just because an LLM’s context window can fit a huge amount of information in it doesn’t mean the LLM can make effective use of that information. The appeal of RAG is its precision. For industries where accuracy is at a premium—like healthcare or the legal profession—simply having a huge amount of information available doesn’t address the actual needs. It would be like having a set of beautiful encyclopedias open to all pages at all times. If this idea breaks your brain a little, that’s the point. It’s physically impossible. RAG’s ability to quickly (and cost-effectively) provide exactly the information required for a certain task means that it’s highly likely to stay relevant long into the future, regardless of how large context windows may grow. 🧠 Check out our (In)frequently Asked Questions page for more smart, concise answers to interesting debates about AI: https://poma-ai.com/#iaqs 🧠

The Ultimate Guide to Advanced RAG Techniques! If you’re building Retrieval-Augmented Gen AI systems, this curated repo Awesome-RAG is gold. It goes beyond basics and collects top-tier techniques like: - Corrective RAG (CRAG), Plan-then-RAG, GraphRAG, Agentic RAG - Smart chunking: semantic, recursive, document-based - Embedding model selection, re-ranking, and safety measures such as hallucination checks, guardrails, etc. GitHub These aren’t “nice to know”, they’re what differentiate mediocre RAG setups from production-grade, reliable ones. Link in comments. thanks Daniel Skryseth #RAG #AIEngineering #GenerativeAI #SystemReliability