Techniques for Improving AI Recommendation Accuracy

If you are an AI Engineer building production-grade GenAI systems, RAG should be in your toolkit. LLMs are powerful for information generation, but: → They hallucinate → They don’t know anything post-training → They struggle with out-of-distribution queries RAG solves this by injecting external knowledge at inference time. But basic RAG (retrieval + generation) isn’t enough for complex use cases. You need advanced techniques to make it reliable in production. Let’s break it down 👇 🧠 Basic RAG = Retrieval → Generation You ask a question. → The retriever fetches top-k documents (via vector search, BM25, etc.) → The LLM answers based on the query + retrieved context But, this naive setup fails quickly in the wild. You need to address two hard problems: 1. Are we retrieving the right documents? 2. Is the generator actually using them faithfully? ⚙️ Advanced RAG = Engineering Both Ends To improve retrieval, we have techniques like: → Chunk size tuning (fixed vs. recursive splitting) → Sliding window chunking (for dense docs) → Structured data retrieval (tables, graphs, SQL) → Metadata-aware search (filtering by author/date/type) → Mixed retrieval (hybrid keyword + dense) → Embedding fine-tuning (aligning to domain-specific semantics) → Question rewriting (to improve recall) To improve generation, options include: → Compressing retrieved docs (summarization, reranking) → Generator fine-tuning (rewarding citation usage and reasoning) → Re-ranking outputs (scoring factuality or domain accuracy) → Plug-and-play adapters (LoRA, QLoRA, etc.) 🧪 Beyond Modular: Joint Optimization Some of the most promising work goes further: → Fine-tuning retriever + generator end-to-end → Retrieval training via generation loss (REACT, RETRO-style) → Generator-enhanced search (LLM reformulates the query for better retrieval) This is where RAG starts to feel less like a bolt-on patch and more like a full-stack system. 📏 How Do You Know It's Working? Key metrics to track: → Context Relevance (Are the right docs retrieved?) → Answer Faithfulness (Did the LLM stay grounded?) → Negative Rejection (Does it avoid answering when nothing relevant is retrieved?) → Tools: RAGAS, FaithfulQA, nDCG, Recall@k 🛠️ Arvind and I are kicking off a hands-on workshop on RAG This first session is designed for beginner to intermediate practitioners who want to move beyond theory and actually build. Here’s what you’ll learn: → How RAG enhances LLMs with real-time, contextual data → Core concepts: vector DBs, indexing, reranking, fusion → Build a working RAG pipeline using LangChain + Pinecone → Explore no-code/low-code setups and real-world use cases If you're serious about building with LLMs, this is where you start. 📅 Save your seat and join us live: https://lnkd.in/gS_B7_7d Image source: LlamaIndex

Many companies have started experimenting with simple RAG systems, probably as their first use case, to test the effectiveness of generative AI in extracting knowledge from unstructured data like PDFs, text files, and PowerPoint files. If you've used basic RAG architectures with tools like LlamaIndex or LangChain, you might have already encountered three key problems: 𝟭. 𝗜𝗻𝗮𝗱𝗲𝗾𝘂𝗮𝘁𝗲 𝗘𝘃𝗮𝗹𝘂𝗮𝘁𝗶𝗼𝗻 𝗠𝗲𝘁𝗿𝗶𝗰𝘀: Existing metrics fail to catch subtle errors like unsupported claims or hallucinations, making it hard to accurately assess and enhance system performance. 𝟮. 𝗗𝗶𝗳𝗳𝗶𝗰𝘂𝗹𝘁𝘆 𝗛𝗮𝗻𝗱𝗹𝗶𝗻𝗴 𝗖𝗼𝗺𝗽𝗹𝗲𝘅 𝗤𝘂𝗲𝘀𝘁𝗶𝗼𝗻𝘀: Standard RAG methods often struggle to find and combine information from multiple sources effectively, leading to slower responses and less relevant results. 𝟯. 𝗦𝘁𝗿𝘂𝗴𝗴𝗹𝗶𝗻𝗴 𝘁𝗼 𝗨𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱 𝗖𝗼𝗻𝘁𝗲𝘅𝘁 𝗮𝗻𝗱 𝗖𝗼𝗻𝗻𝗲𝗰𝘁𝗶𝗼𝗻𝘀: Basic RAG approaches often miss the deeper relationships between information pieces, resulting in incomplete or inaccurate answers that don't fully meet user needs. In this post I will introduce three useful papers to address these gaps: 𝟭. 𝗥𝗔𝗚𝗖𝗵𝗲𝗸𝗲𝗿: introduces a new framework for evaluating RAG systems with a focus on fine-grained, claim-level metrics. It proposes a comprehensive set of metrics: claim-level precision, recall, and F1 score to measure the correctness and completeness of responses; claim recall and context precision to evaluate the effectiveness of the retriever; and faithfulness, noise sensitivity, hallucination rate, self-knowledge reliance, and context utilization to diagnose the generator's performance. Consider using these metrics to help identify errors, enhance accuracy, and reduce hallucinations in generated outputs. 𝟮. 𝗘𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝘁𝗥𝗔𝗚: It uses a labeler and filter mechanism to identify and retain only the most relevant parts of retrieved information, reducing the need for repeated large language model calls. This iterative approach refines search queries efficiently, lowering latency and costs while maintaining high accuracy for complex, multi-hop questions. 𝟯. 𝗚𝗿𝗮𝗽𝗵𝗥𝗔𝗚: By leveraging structured data from knowledge graphs, GraphRAG methods enhance the retrieval process, capturing complex relationships and dependencies between entities that traditional text-based retrieval methods often miss. This approach enables the generation of more precise and context-aware content, making it particularly valuable for applications in domains that require a deep understanding of interconnected data, such as scientific research, legal documentation, and complex question answering. For example, in tasks such as query-focused summarization, GraphRAG demonstrates substantial gains by effectively leveraging graph structures to capture local and global relationships within documents. It's encouraging to see how quickly gaps are identified and improvements are made in the GenAI world.

RAG just got smarter. If you’ve been working with Retrieval-Augmented Generation (RAG), you probably know the basic setup: An LLM retrieves documents based on a query and uses them to generate better, grounded responses. But as use cases get more complex, we need more advanced retrieval strategies—and that’s where these four techniques come in: Self-Query Retriever Instead of relying on static prompts, the model creates its own structured query based on metadata. Let’s say a user asks: “What are the reviews with a score greater than 7 that say bad things about the movie?” This technique breaks that down into query + filter logic, letting the model interact directly with structured data (like Chroma DB) using the right filters. Parent Document Retriever Here, retrieval happens in two stages: 1. Identify the most relevant chunks 2. Pull in their parent documents for full context This ensures you don’t lose meaning just because information was split across small segments. Contextual Compression Retriever (Reranker) Sometimes the top retrieved documents are… close, but not quite right. This reranker pulls the top K (say 4) documents, then uses a transformer + reranker (like Cohere) to compress and re-rank the results based on both query and context—keeping only the most relevant bits. Multi-Vector Retrieval Architecture Instead of matching a single vector per document, this method breaks both queries and documents into multiple token-level vectors using models like ColBERT. The retrieval happens across all vectors—giving you higher recall and more precise results for dense, knowledge-rich tasks. These aren’t just fancy tricks. They solve real-world problems like: • “My agent’s answer missed part of the doc.” • “Why is the model returning irrelevant data?” • “How can I ground this LLM more effectively in enterprise knowledge?” As RAG continues to scale, these kinds of techniques are becoming foundational. So if you’re building search-heavy or knowledge-aware AI systems, it’s time to level up beyond basic retrieval. Which of these approaches are you most excited to experiment with? #ai #agents #rag #theravitshow

Want to Make Your RAG Application 10x Smarter? Retrieval-Augmented Generation (RAG) systems are powerful, however with the right strategies, you can turn them into precision tools. Here’s a breakdown of 10 expert-backed ways to optimize RAG performance: 1. 🔹Use Domain-Specific Embeddings Choose embeddings trained on your industry (like legal, medical, or finance) to improve semantic understanding and relevance. 2. 🔹Chunk Wisely Split documents into overlapping, context-rich chunks. Avoid mid-sentence breaks to preserve meaning during retrieval. 3. 🔹Rerank Results with LLMs Instead of relying only on top vector matches, rerank retrieved chunks using your LLM and a scoring prompt. 4. 🔹Add Metadata Filtering Use filters (like author, date, or doc type) to refine results before sending them to your language model. 5. 🔹Use Hybrid Search (Vector + Keyword) Combine the precision of keyword search with the flexibility of vector search to boost accuracy and recall. [Explore More In The Post] ✅ Use this checklist to fine-tune your RAG workflows, reduce errors, and deliver smarter, more reliable AI responses. #genai #artificialintelligence