How to Improve Retrieval-Augmented Generation Architectures

If you're an AI engineer building RAG pipelines, this one’s for you. RAG has evolved from a simple retrieval wrapper into a full-fledged architecture for modular reasoning. But many stacks today are still too brittle, too linear, and too dependent on the LLM to do all the heavy lifting. Here’s what the most advanced systems are doing differently 👇 🔹 Naïve RAG → One-shot retrieval, no ranking or summarization. → Retrieved context is blindly appended to prompts. → Breaks under ambiguity, large corpora, or multi-hop questions. → Works only when the task is simple and the documents are curated. 🔹 Advanced RAG → Adds pre-retrieval modules (query rewriting, routing, expansion) to tighten the search space. → Post-processing includes reranking, summarization, and fusion, reducing token waste and hallucinations. → Often built using DSPy, LangChain Expression Language, or custom prompt compilers. → Far more robust, but still sequential, limited adaptivity. 🔹 Modular RAG → Not a pipeline- a DAG of reasoning operators. → Think: Retrieve, Rerank, Read, Rewrite, Memory, Fusion, Predict, Demonstrate. → Built for interleaved logic, recursion, dynamic routing, and tool invocation. → Powers agentic flows where reasoning is distributed across specialized modules, each tunable and observable. Why this matters now ⁉️ → New LLMs like GPT-4o, Claude 3.5 Sonnet, and Mistral 7B Instruct v2 are fast — so bottlenecks now lie in retrieval logic and context construction. → Cohere, Fireworks, and Together are exposing rerankers and context fusion modules as inference primitives. → LangGraph and DSPy are pushing RAG into graph-based orchestration territory — with memory persistence and policy control. → Open-weight models + modular RAG = scalable, auditable, deeply controllable AI systems. 💡 Here are my 2 cents- for engineers shipping real-world LLM systems: → Upgrade your retriever, not just your model. → Optimize context fusion and memory design before reaching for finetuning. → Treat each retrieval as a decision, not just a static embedding call. → Most teams still rely on prompting to patch weak context. But the frontier of GenAI isn’t prompt hacking, it’s reasoning infrastructure. Modular RAG brings you closer to system-level intelligence, where retrieval, planning, memory, and generation are co-designed. 🛠️ 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

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

Most people do not look beyond the basic RAG pipeline, and it rarely works out as expected! RAG is known to lack robustness due to the LLM weaknesses, but it doesn't mean we cannot build robust pipelines! Here is how we can improve them. The RAG pipeline, in its simplest form, is composed of a retriever and a generator. The user question is used to retrieve the database data that could be used as context to answer the question better. The retrieved data is used as context in a prompt for an LLM to answer the question. Instead of using the original user question as a query to the database, it is typical to rewrite the question for optimized retrieval. Instead of blindly returning the answer to the user, we better assess the generated answer. That is the idea behind Self-RAG. We can check for hallucinations and relevance to the question. If the model hallucinates, we are going to try again the generation, and if the answer doesn't address the question, we are going to restart the retrieval by rewriting the query. If the answer passes the validation, we can return it to the user. It might be better to provide feedback for the new retrieval and the new generation to be performed in a more educated manner. In the case we have too many iterations, we are going to assume that we just reach a state where the model will apologize for not being able to provide an answer to the question. When we are retrieving the documents, we are likely retrieving irrelevant documents, so it could be a good idea to filter only the relevant ones before providing them to the generator. Once the documents are filtered, it is likely that a lot of the information contained in the documents is irrelevant, so it is also good to extract only what could be useful to answer the question from the documents. This way, the generator will only see relevant information to answer the question. The assumption in typical RAG is that the question will be about the data stored in the database, but this is a very rigid assumption. We can use the idea behind Adaptive-RAG, where we are going to assess the question first and route to a datastore RAG, a websearch or a simple LLM. It is possible that we realize that none of the documents are actually relevant to the question, and we better reroute the question back to the web search. That is part of the idea behind Corrective RAG. If we reach the maximum of web search retries, we can give up and apologize to the user. Here is how I implemented this pipeline with LangGraph: https://lnkd.in/g8AAF7Fw