How to ensure accurate data for LLMs/RAGs

🚀 Making Document Retrieval Smarter: How Chunks Pull Context from Their Parent Imagine each document as a tree. Each chunk is a branch, holding its own details. But without context, a branch alone can be misleading. Our approach: we let chunks “pull context” from their parent document, blending their local details with the global picture. How we optimized it: 1️⃣ Precompute Once – All chunks are blended with their parent document embeddings ahead of time, stored in a single ChromaDB collection. No recomputation per query. 2️⃣ Similarity-Weighted Blending – Chunks closer to the document’s core content pull more context, while minor chunks retain their unique info. 3️⃣ Batch Processing – Blending and storing in batches ensures speed and memory efficiency. 4️⃣ Fast Query Retrieval – Queries simply fetch the enhanced chunks. No dynamic computation, no redundant writes. 💡 Why it matters: Each chunk now has both local detail and global context, improving retrieval accuracy. Works especially well in finance, legal, and pharma, where context is key. High-quality retrieval without retraining models or using heavy cross-encoders. #RAG #ChromaDB #AI #MachineLearning #LLM #NLP #VectorDatabases #DocumentRetrieval #KnowledgeManagement #DataScience #EmbeddingOptimization #DeepLearning

"𝑾𝒉𝒚 𝒅𝒊𝒅 𝒕𝒉𝒊𝒔 𝒗𝒊𝒅𝒆𝒐 𝒈𝒆𝒕 𝒓𝒂𝒏𝒌𝒆𝒅 𝒇𝒊𝒓𝒔𝒕?" is a question most retrieval systems can’t really answer. I am super excited to share our latest work "𝐗-𝐂𝐨𝐓: 𝐄𝐱𝐩𝐥𝐚𝐢𝐧𝐚𝐛𝐥𝐞 𝐓𝐞𝐱𝐭-𝐭𝐨-𝐕𝐢𝐝𝐞𝐨 𝐑𝐞𝐭𝐫𝐢𝐞𝐯𝐚𝐥 𝐯𝐢𝐚 𝐋𝐋𝐌-𝐛𝐚𝐬𝐞𝐝 𝐂𝐡𝐚𝐢𝐧-𝐨𝐟-𝐓𝐡𝐨𝐮𝐠𝐡𝐭 𝐑𝐞𝐚𝐬𝐨𝐧𝐢𝐧𝐠" published in 𝐄𝐌𝐍𝐋𝐏 𝟮𝟬𝟮𝟱 (Main) Conference. Instead of relying only on cosine similarity scores from embedding models, 𝐗-𝐂𝐨𝐓 𝐚𝐬𝐤𝐬 𝐚𝐧 𝐋𝐋𝐌 𝐭𝐨 𝒕𝒉𝒊𝒏𝒌 𝒕𝒉𝒓𝒐𝒖𝒈𝒉 𝐭𝐡𝐞 𝐫𝐚𝐧𝐤𝐢𝐧𝐠, 𝐫𝐞𝐫𝐚𝐧𝐤 𝐚𝐧𝐝 𝐞𝐱𝐩𝐥𝐚𝐢𝐧 𝘸𝘩𝘺 one video should be preferred over another. The goal is not just higher retrieval metrics, but rankings that come with human-readable reasons. What X-CoT does: - 𝐔𝐬𝐞𝐬 𝐋𝐋𝐌-𝐛𝐚𝐬𝐞𝐝 𝐩𝐚𝐢𝐫𝐰𝐢𝐬𝐞 reasoning to build a full video ranking. - Produces 𝐡𝐮𝐦𝐚𝐧-𝐫𝐞𝐚𝐝𝐚𝐛𝐥𝐞 𝐫𝐚𝐭𝐢𝐨𝐧𝐚𝐥𝐞𝐬 for each comparison, so you can see 𝘸𝘩𝘺 a candidate is above or below another. - Uses the explanations to 𝐬𝐩𝐨𝐭 𝐛𝐚𝐝 𝐨𝐫 𝐛𝐢𝐚𝐬𝐞𝐝 𝐭𝐞𝐱𝐭-𝐯𝐢𝐝𝐞𝐨 𝐩𝐚𝐢𝐫𝐬 and analyze model behavior, not just metrics. Data contributions: - We 𝐞𝐱𝐩𝐚𝐧𝐝 𝐞𝐱𝐢𝐬𝐭𝐢𝐧𝐠 𝐭𝐞𝐱𝐭-𝐭𝐨-𝐯𝐢𝐝𝐞𝐨 𝐛𝐞𝐧𝐜𝐡𝐦𝐚𝐫𝐤𝐬 with extra video annotations that improve semantic coverage. - The dataset is publicly released on HuggingFace to support future work on 𝐞𝐱𝐩𝐥𝐚𝐢𝐧𝐚𝐛𝐥𝐞 𝐯𝐢𝐝𝐞𝐨 𝐫𝐞𝐭𝐫𝐢𝐞𝐯𝐚𝐥 𝐚𝐧𝐝 𝐋𝐋𝐌 𝐫𝐞𝐚𝐬𝐨𝐧𝐢𝐧𝐠. Links and resources: - Paper: https://lnkd.in/ge8XNmgW - Code: https://lnkd.in/gfpfptYe - Project Page: https://lnkd.in/gZzaNGzu - HuggingFace Dataset: https://lnkd.in/gm7i98v7 Grateful to work with an amazing team: Jiamian (Aloes) Wang, Dr. Majid Rabbani, Dr. Sohail Dianat, Dr. Raghuveer Rao, and Dr. Zhiqiang Tao. If you are working on multimodal retrieval, LLM reasoning, or explainable AI, I would love to hear your feedback and thoughts. And if you find X-CoT useful, please try it out, share it, and consider citing it! #EMNLP2025 #ExplainableAI #LLM #ChainOfThought #Multimodal #VideoRetrieval #NLP

🤖 RAG vs Fine-tuning: Which AI approach should you choose? 💡 RAG (Retrieval-Augmented Generation) Fetches real-time information (from PDFs, web, docs, APIs, etc.) to answer your query, without retraining the model. Perfect for dynamic, ever-changing data. 💡 Fine-tuning Trains the model offline with domain-specific data — the model learns permanently, offering deeper expertise but requiring more time and compute. ⚖️ The Bottom Line: - Need the latest information? → Go with RAG - Need specialized expertise? → Choose Fine-tuning 👨💻The future of AI isn't choosing one over the other, it's knowing when to use each approach. Follow Akash Shahade for more simple and practical AI breakdowns.🤖 #AI #MachineLearning #LLM #FineTuning #RAG #GenAI #ArtificialIntelligence #AIInsights #DeepLearning #TechInnovation #DataScience #NLP #AIEngineering #TechLeadership

🔥 One Hot Encoding — Simplifying Text for Machines One-hot encoding is a simple yet powerful technique used to convert categorical values (like words or labels) into numerical vectors that machine learning algorithms can understand. 📘 Example: Let’s say we have three sentences: D1: This is Bad Food D2: This is Good Food D3: This is Amazing Pizza 👉 We first build a vocabulary of all unique words: ["This", "is", "bad", "food", "good", "amazing", "pizza"] Then, each word (or document) is represented as a binary vector — 1 means the word is present, 0 means it’s not. "This"  -> [1,0,0,0,0,0,0] "is"   -> [0,1,0,0,0,0,0] "bad"   -> [0,0,1,0,0,0,0] "food"  -> [0,0,0,1,0,0,0] "good"  -> [0,0,0,0,1,0,0] "amazing" -> [0,0,0,0,0,1,0] "pizza"  -> [0,0,0,0,0,0,1] #AI #MachineLearning #DeepLearning #NLP #DataScience #GenerativeAI #LearningTogether #OneHotEncoding #TextProcessing #FeatureEngineering

Retrieval-Augmented Generation (RAG) is one of the most practical ways to make LLMs smarter by grounding their answers in external knowledge. A Naïve RAG setup is the simplest implementation – great for getting started. Here’s how it works: 1️⃣ Break your documents into chunks and embed them into a vector database. 2️⃣ When a query comes in, convert it into an embedding and perform a similarity search. 3️⃣ Retrieve the top-k chunks and pass them directly into the LLM prompt. 4️⃣ The LLM generates an answer based on both the query and retrieved context. It’s “naïve” because everything is direct – no reranking, filtering, or query rewriting. While it may bring in irrelevant chunks, it provides a solid baseline for experimentation. From here, teams usually add smarter retrieval strategies to improve accuracy and reduce noise. 👉 Start simple. Scale smart. #RAG #RetrievalAugmentedGeneration #NaiveRAG #GenerativeAI #LLM #VectorDatabase #AI #ArtificialIntelligence #MachineLearning #NLP #TechTrends #UNext

🚀 Improving Retrieval with Contextual Compression One of the biggest challenges in retrieval-based systems is handling irrelevant context. When we ingest data, we rarely know what specific queries will be asked later — meaning the most relevant information might be buried inside long, noisy documents. Passing these full documents to an LLM can lead to: ❌ Higher token and compute costs ❌ Poorer response quality due to irrelevant context That’s where Contextual Compression comes in. 💡 The concept is simple but powerful: instead of returning retrieved documents as-is, we compress them in context of the query — keeping only the parts that truly matter. Here’s how it works: 1. Base Retriever → fetches the initial set of documents. 2. Document Compressor → filters and shortens them, keeping only relevant content (and even dropping entire documents if needed). The result? ✅ More focused, high-quality responses ✅ Lower LLM costs ✅ Smarter, context-aware retrieval Contextual compression ensures your retrieval pipeline delivers precise, efficient, and scalable intelligence — not just more data. #AI #RetrievalAugmentedGeneration #LLM #LangChain #ContextualCompression #MachineLearning #NLP #GenerativeAI

Exploring How LLMs Think: A Tiny Model vs a Giant Model I recently experimented with two language models — a small 0.6B LLM (Qwen3) and a 20B LLM (GPT-OSS) — using LangChain discovered something fascinating about how AI “thinks.” Even a tiny 0.6B model can summarize short texts almost as accurately as a 20B model. But when I: enabled reasoning, increased context length, changed the task, or used tools …the outputs started to diverge. This showed me something important: Each change reveals which part of a model’s intelligence is activated. and when i asked chatgpt can you rate the model without telling which one is big or small its rates like this : Model 2 → 9.5/10 (best overall — clear, precise, natural, and detailed) Model 1 → 9/10 (very strong, just slightly less natural and detailed) Even small experiments like this — when structured properly with LangChain — teach deep insights into LLM behavior. It’s amazing how much you can learn by observing output differences and structuring prompts systematically. I’m excited to continue exploring AI reasoning, context handling, and task-specific behavior with structured pipelines. #MachineLearning #AI #LLM #NLP #DeepLearning #LangChain #LearningByDoing #Python

𝗙𝗶𝗻𝗲-𝗧𝘂𝗻𝗶𝗻𝗴 𝘃𝘀. 𝗣𝗿𝗼𝗺𝗽𝘁 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴, 𝗪𝗵𝗮𝘁 𝗥𝗲𝗮𝗹𝗹𝘆 𝗦𝗰𝗮𝗹𝗲𝘀 𝗶𝗻 𝗔𝗜? One of the most common debates in modern AI development: Should you fine-tune your model, or just engineer better prompts? Both approaches improve performance, but they solve different problems. ★ 𝗣𝗿𝗼𝗺𝗽𝘁 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 works when: - You’re adapting a general model (like GPT) for new tasks. - You need contextual control without retraining. - You prioritize speed and flexibility. ★ 𝗙𝗶𝗻𝗲-𝗧𝘂𝗻𝗶𝗻𝗴 works when: - You have domain-specific data (e.g., legal, medical, finance). - You need consistent output styles or deeper reasoning. - You can afford compute and versioning overhead. The trade-off: Prompt engineering scales creativity and experimentation, while fine-tuning scales consistency and performance. The real power lies in 𝗛𝘆𝗯𝗿𝗶𝗱 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻 fine-tuned models guided by prompt frameworks that add context, constraints, and reasoning structure. ★ In 2025, the best AI teams aren’t choosing between the two, they’re mastering both. #PromptEngineering #FineTuning #LLM #ArtificialIntelligence #AIEngineering #MachineLearning #DeepLearning #NLP #AIModels #DataScience #SoftwareDevelopment #Automations

🚀 Excited to share my latest project: OCR + RAG Assistant! This is a cloud-ready AI assistant that can answer questions directly from scanned documents. It combines: 🔍 FAISS for semantic search over OCR documents 🤖 LLaMA 3.3 70B Versatile (via Groq API) for advanced text generation ⚡ FastAPI backend serving a clean API No heavy LLM runs locally — everything is cloud-ready and scalable. 💡 Future improvements: Automatic document upload and indexing Deploying a public API for wider usage Adding a user-friendly frontend Check it out on GitHub: https://lnkd.in/errxqwx5 #AI #MachineLearning #RAG #OCR #FAISS #LLaMA #Groq #FastAPI #NLP

Does Prompt Formatting Really Matter for LLM Performance? 📝🤖 Recently, in our exploration of GPT-based Large Language Models (LLMs), we discovered something surprising but critical: prompt formatting can dramatically impact model performance—sometimes up to a staggering 40% difference! Key findings from the latest research (He et al., Microsoft/MIT, Nov 2024): Prompt formats matter: Whether you use plain text, Markdown, YAML, or JSON, the structure of your prompt influences accuracy, reliability, and consistency. No universal format: Each GPT model (from 3.5 to 4 series) reacts differently; for example, GPT-3.5-turbo performs best with JSON, while GPT-4 prefers Markdown. Model size matters: Larger models like GPT-4 are generally more robust to prompt changes, but still not immune! Evaluation needs to change: Fixed prompt templates may lead to misleading benchmarks—diversifying prompt formats is essential for fair model testing. If you’re designing AI systems, developing NLP applications, or benchmarking LLMs, don’t treat prompt formatting as a cosmetic detail. It’s a lever for real performance gains! 🔎 Check out the full study for insights and practical templates. Let's step up our prompt engineering game! #AI #NLP #PromptEngineering #LLMs #MachineLearning #Research #Productivity