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I've been building and deploying RAG systems for 2+ years. And it's taught me optimizing them requires focusing on 3 core stages: 1. Pre-Retrieval 2. Retrieval 3. Post-Retrieval Let me explain - Most people focus on the generation side of things. But optimizing retrieval is what really makes the difference. Here's how to do it: 𝟭/ 𝗣𝗿𝗲-𝗿𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹 This is where we optimize the data before the retrieval process even begins. The goal? Structure your data for efficient indexing and ensure the query is as precise as possible before it's embedded and sent to your vector DB. Here’s how: - 𝗦𝗹𝗶𝗱𝗶𝗻𝗴 𝘄𝗶𝗻𝗱𝗼𝘄: 𝘐𝘯𝘵𝘳𝘰𝘥𝘶𝘤𝘦 𝘤𝘩𝘶𝘯𝘬 𝘰𝘷𝘦𝘳𝘭𝘢𝘱 𝘵𝘰 𝘳𝘦𝘵𝘢𝘪𝘯 𝘤𝘰𝘯𝘵𝘦𝘹𝘵 𝘢𝘯𝘥 𝘪𝘮𝘱𝘳𝘰𝘷𝘦 𝘳𝘦𝘵𝘳𝘪𝘦𝘷𝘢𝘭 𝘢𝘤𝘤𝘶𝘳𝘢𝘤𝘺. - 𝗘𝗻𝗵𝗮𝗻𝗰𝗶𝗻𝗴 𝗱𝗮𝘁𝗮 𝗴𝗿𝗮𝗻𝘂𝗹𝗮𝗿𝗶𝘁𝘆: 𝘊𝘭𝘦𝘢𝘯, 𝘷𝘦𝘳𝘪𝘧𝘺, 𝘢𝘯𝘥 𝘶𝘱𝘥𝘢𝘵𝘦 𝘥𝘢𝘵𝘢 𝘧𝘰𝘳 𝘴𝘩𝘢𝘳𝘱𝘦𝘳 𝘳𝘦𝘵𝘳𝘪𝘦𝘷𝘢𝘭. - 𝗠𝗲𝘁𝗮𝗱𝗮𝘁𝗮: 𝘜𝘴𝘦 𝘵𝘢𝘨𝘴 (𝘭𝘪𝘬𝘦 𝘥𝘢𝘵𝘦𝘴 𝘰𝘳 𝘦𝘹𝘵𝘦𝘳𝘯𝘢𝘭 𝘐𝘋𝘴) 𝘵𝘰 𝘪𝘮𝘱𝘳𝘰𝘷𝘦 𝘧𝘪𝘭𝘵𝘦𝘳𝘪𝘯𝘨. - 𝗦𝗺𝗮𝗹𝗹-𝘁𝗼-𝗯𝗶𝗴 (or parent) 𝗶𝗻𝗱𝗲𝘅𝗶𝗻𝗴: 𝘜𝘴𝘦 𝘴𝘮𝘢𝘭𝘭𝘦𝘳 𝘤𝘩𝘶𝘯𝘬𝘴 𝘧𝘰𝘳 𝘦𝘮𝘣𝘦𝘥𝘥𝘪𝘯𝘨 𝘢𝘯𝘥 𝘭𝘢𝘳𝘨𝘦𝘳 𝘤𝘰𝘯𝘵𝘦𝘹𝘵𝘴 𝘧𝘰𝘳 𝘵𝘩𝘦 𝘧𝘪𝘯𝘢𝘭 𝘢𝘯𝘴𝘸𝘦𝘳. - 𝗤𝘂𝗲𝗿𝘆 𝗼𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻: 𝘛𝘦𝘤𝘩𝘯𝘪𝘲𝘶𝘦𝘴 𝘭𝘪𝘬𝘦 𝘲𝘶𝘦𝘳𝘺 𝘳𝘰𝘶𝘵𝘪𝘯𝘨, 𝘲𝘶𝘦𝘳𝘺 𝘳𝘦𝘸𝘳𝘪𝘵𝘪𝘯𝘨, 𝘢𝘯𝘥 𝘏𝘺𝘋𝘌 𝘤𝘢𝘯 𝘳𝘦𝘧𝘪𝘯𝘦 𝘵𝘩𝘦 𝘳𝘦𝘴𝘶𝘭𝘵𝘴. 𝟮/ 𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹 The magic happens here. Your goal is to improve the embedding models and leverage DB filters to retrieve the most relevant data based on semantic similarity. - Fine-tune your embedding models or use instructor models like instructor-xl for domain-specific terms. - Use hybrid search to blend vector and keyword search for more precise results. - Use GraphDBs or multi-hop techniques to capture relationships within your data. 𝟯. 𝗣𝗼𝘀𝘁-𝗿𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹 At this stage, your task is to filter out noise and compress the final context before sending it to the LLM. - Use prompt compression techniques. - Filter out irrelevant chunks to avoid adding noise to the augmented prompt (e.g., using reranking) 𝗥𝗲𝗺𝗲𝗺𝗯𝗲𝗿: RAG optimization is an iterative process. Experiment with various techniques, measure their effectiveness, compare them and refine them. Ready to step up your RAG game? Check out the link in the comments.

You're in an AI Engineer Interview. Interviewer: Your RAG retrieval is too slow with a large knowledge base. How do you speed it up? Here's how I'd approach: I optimize retrieval at multiple layers rather than relying on a single fix. ✅ Use a hybrid retrieval strategy Combine vector search with keyword-based retrieval (BM25) to improve relevance while reducing unnecessary searches. ✅ Tune chunking and indexing Smaller, well-structured chunks improve retrieval accuracy and reduce the number of documents that need re-ranking. ✅ Apply metadata filtering Filter documents by source, date, product, region, or category before vector search to shrink the search space. ✅ Use Approximate Nearest Neighbor (ANN) indexes Technologies like HNSW and IVF drastically reduce search latency compared to brute-force similarity searches. ✅ Implement multi-stage retrieval Retrieve a small candidate set first, then apply cross-encoder re-ranking only on the top results. ✅ Cache frequent queries Many enterprise questions repeat. Caching embeddings and retrieval results can significantly cut response times. ✅ Optimize embeddings Use efficient embedding models and periodically re-evaluate whether higher-dimensional vectors are actually improving retrieval quality. ✅ Monitor retrieval metrics Track latency, recall@k, hit rate, and re-ranking time to identify bottlenecks before they impact users. The biggest mistake is trying to solve retrieval speed by upgrading hardware alone. #AI #GenAI #RAG #RetrievalAugmentedGeneration #LLM #MachineLearning #DataScience #AIEngineering #VectorDatabase #ArtificialIntelligence Follow Sneha Vijaykumar for more...😊

I've reviewed 50+ #RecSys architectures. The most expensive mistake I keep seeing: Using a 70B LLM to do real-time ranking. It fails in production. Every time. Here's why and what actually works instead 👇 At Amazon and Twitter, we operated under strict latency budgets. You cannot run autoregressive inference over a massive catalog in ~50ms. That's not a modeling problem. That's physics. The real issue isn't that LLMs are weak. It's that they're being used in the wrong place. The pattern is called LLM-ERS (LLM-Enhanced Recommender Systems): Layer 1 - Retrieval + Ranking (keep it traditional) Fast. Scalable. No LLM needed here. → Two-Tower embeddings → Vector DB retrieval → Top ~1K candidates in sub-10ms Layer 2 - Offline Data Augmentation (highest ROI) Move the LLM off the critical path entirely. Use it to improve your training data instead: → Generate hard negatives for triplet loss → Plausible-but-wrong items that force tighter decision boundaries Zero latency cost. Massive modeling gain. Layer 3 - Post-Ranking Personalization (controlled LLM use) If you need real-time LLMs, keep the blast radius small: → Take only your top 3–5 ranked items → Generate "Why you might like this" explanations You get personalization + better UX - without breaking P99. The rule that separates robust systems from expensive ones: → Traditional ML decides what to show → LLMs enhance how it's understood Boring architecture for scale. LLMs for the final mile. This is Part 3 of my Generative RecSys series. Next up: I'll break down the Generative Retrieval Paradigm. I'm genuinely curious: where in your stack have LLMs actually moved the needle vs. just adding cost and latency? Drop your answer + one sentence on why. A) Retrieval B) Ranking C) Explanation / UX layer D) Offline data augmentation E) Nowhere yet - still evaluating

Most Retrieval-Augmented Generation (RAG) systems still optimize for the final answer. But what if an agent’s real intelligence lies not in what it answers, but how it decides to search? That’s the premise behind “HiPRAG: Hierarchical Process Rewards for Efficient Agentic Retrieval-Augmented Generation” (Peilin Wu et al., UT Dallas and Adobe, 2025). Instead of rewarding correctness alone, HiPRAG teaches models to reason about when to retrieve, when to rely on internal knowledge, and when to stop searching altogether. HiPRAG structures each reasoning trajectory into parsable <step> blocks, labeling every action as search or non-search. This enables direct supervision over the reasoning process itself. Its hierarchical reward design combines three objectives: 1) Outcome reward – final answer correctness. 2) Format reward – structural validity and adherence to the reasoning schema. 3) Process reward – efficiency across reasoning steps, penalizing both redundant searches (over-search) and missed retrievals (under-search). Inefficiencies are automatically detected through LLM-based verifiers that simulate counterfactuals: 1) If removing a retrieval still yields the same result → over-search. 2) If skipping retrieval leads to an incorrect step → under-search. Training uses Proximal Policy Optimization (PPO) and Generalized Reward Policy Optimization (GRPO): 1) PPO stabilizes learning by constraining policy updates through clipped objectives. 2) GRPO extends PPO to handle multi-dimensional, hierarchical rewards, making it well-suited for HiPRAG’s layered supervision. A hierarchical gating mechanism ensures that process rewards are applied only after the model generates a correct and well-structured trace - improving stability and interpretability. Across seven QA benchmarks (NQ, HotpotQA, 2Wiki, Musique, etc.), HiPRAG reduces over-search from 27% → 2.3%, decreases under-search to ~32%, and improves average accuracy by 5–7% over Search-R1 baselines. These results hold consistently across Qwen2.5 (3B/7B) and Llama3.2 models. HiPRAG moves Reinforcement Learning for LLMs from outcome-centric to process-centric optimization. It rewards agents not merely for getting the right answer, but for reasoning efficiently - for knowing when to search, and when to stop. That subtle shift may define the next phase of intelligent retrieval agents. Full paper: arXiv:2510.07794 Code: https://lnkd.in/gw4kw8HF #ReinforcementLearning #RetrievalAugmentedGeneration #AgenticAI #LLMResearch #ReasoningModels #HiPRAG #RLHF #PPO #GRPO #AIResearch #MachineLearning #KnowledgeRetrieval #EfficientAI #ReasoningOptimization #ProcessLevelRL #ArtificialIntelligence

Exciting Research Alert: Efficient Constant-Space Multi-Vector Retrieval Just came across this groundbreaking paper from researchers at University of Glasgow, Pinecone, and University of Pisa that tackles one of the biggest challenges in multi-vector retrieval systems: storage efficiency! Multi-vector retrieval methods like ColBERT have shown impressive results in information retrieval, but they come with a significant storage cost - storing a vector for every token in a document collection quickly becomes unwieldy. The researchers propose a novel approach called ConstBERT that encodes documents to a fixed number of vectors instead of token-tied representations. Here's how it works under the hood: 1. Rather than using traditional token-level embeddings (one per token), ConstBERT introduces a learned pooling mechanism that projects these token embeddings into a reduced set of document-level embeddings. 2. The technique uses a linear transformation with parameters W that takes token embeddings and projects them to a fixed number (C) of embeddings of the same dimensions. 3. Each of these new embeddings encodes different semantic facets of the document, creating a more compact but still effective representation. 4. The relevance score between query and document is computed using a late interaction mechanism that sums the maximum similarity between each query token and the document's fixed set of vectors. The results are impressive: - ConstBERT with just 32 vectors per document achieves comparable effectiveness to ColBERT on MSMARCO and BEIR benchmarks - Index size reductions of over 50% compared to ColBERT at equivalent effectiveness - Better OS paging management due to fixed-size document representations - Particularly effective as a reranking method with mean response times below 6ms This approach is complementary to other compression methods like dimensionality reduction, making it a practical solution for real-world information retrieval applications where both storage efficiency and retrieval speed are essential. The code is available at GitHub

Keyword search just got 10x faster by being... lazier? My amazing colleagues at Weaviate reducing keyword search time by 10x while using 90% less storage. 𝗧𝗵𝗲 𝗜𝗻𝗻𝗼𝘃𝗮𝘁𝗶𝗼𝗻 BlockMax WAND isn't just an incremental improvement - it's a fundamental rethinking of document scoring. By dividing posting lists into blocks with local max impact, it creates a hierarchical optimisation that wasn't possible before. 𝗧𝗵𝗲 𝗡𝘂𝗺𝗯𝗲𝗿𝘀  • Traditional WAND: Inspects 15-30% of documents • BlockMax WAND: Only 5-15% of documents • Query time reduction: 80-94% faster • Storage reduction: 50-90% smaller indices What makes this significant is how it elegantly solves the classic space-time tradeoff. Instead of choosing between fast queries OR efficient storage, BlockMax WAND achieves both through clever compression techniques like varenc and delta encoding. The algorithm uses block-level metadata to skip entire sections without even loading them from disk. It's like having a table of contents for your index - you know exactly where NOT to look. For researchers working on information retrieval, this opens new possibilities: • Scaling to truly massive datasets becomes feasible • Real-time search in production systems with strict latency requirements • New opportunities for hybrid vector-keyword search optimisation In a world where text corpora are growing exponentially, being able to search billions of documents efficiently isn't just nice to have. It's essential for the future of hybrid search in RAG and AI systems. This isn't just about making search faster. It's about making previously impossible search applications possible. Learn more: https://lnkd.in/eifsqgqt

🖼️ Your vector database may search every paragraph perfectly. Ask it to find one screenshot or video clip, and the whole system can go blind. A text embedding model turns words into vectors, placing similar meanings nearby. Qwen3-VL Embedding does the same thing across text, images, screenshots, visual documents, video, and mixed inputs. So a query like "find the slide where revenue drops after launch" can match a screenshot. An image can retrieve related text. A product description can find a relevant video clip. No manually written tags on every visual asset first. Under the hood it is a dual encoder. Each query and each content item becomes a vector independently. That independence is the whole reason it scales. Encode the collection once, then encode only each new query and search the stored index. The specs: 2B and 8B versions, up to 32k tokens, more than 30 languages, and maximum vector sizes of 2048 and 4096 dimensions. Matryoshka training lets you shorten those vectors to as few as 64 dimensions without retraining. Less storage, faster comparisons, usually some cost to retrieval quality. #Qwen3VL #MultimodalSearch #Embeddings #RAG #VectorSearch #DualEncoder #Matryoshka #InformationRetrieval #ComputerVision #DeepLearning #AI #MLOps In its January 2026 report, Qwen says the 8 billion embedder scored 77.8 overall on MMEB-V2. That is a benchmark, not your production system. These models are considerably larger than many classic embedders, and visual data adds real compute. Test latency, memory use, and retrieval quality on your own content. For text-only search, Qwen's own text-specific 8 billion embedder scored higher on MMTEB. Use the VL model when visual search actually matters. The big idea: RAG no longer has to search only text. Qwen3-VL Embedding makes knowledge stored in pixels and time searchable. It gets you the shortlist. Our detailed LearnOpenCV post covers both models, including setup and code: https://lnkd.in/g98Es85s If your organization is sitting on a decade of screenshots, scanned documents, product photography, or video that nobody can search, that is a retrieval problem before it is a model problem. Whether it lives in e-commerce, manufacturing QA, or a medical imaging archive, my team at Big Vision builds these systems in production. Tell us what you are working on: https://bigvision.ai/