RESEARCH

8 RAG architectures for AI Engineers: (explained with usage) 1) Naive RAG – Retrieves documents purely based on vector similarity between the query embedding and stored embeddings. – Works best for simple, fact-based queries where direct semantic matching suffices. 2) Multimodal RAG – Handles multiple data types (text, images, audio, etc.) by embedding and retrieving across modalities. – Ideal for cross-modal retrieval tasks like answering a text query with both text and image context. 3) HyDE (Hypothetical Document Embeddings) – Queries are not semantically similar to documents. – This technique generates a hypothetical answer document from the query before retrieval. – Uses this generated document’s embedding to find more relevant real documents. 4) Corrective RAG – Validates retrieved results by comparing them against trusted sources (e.g., web search). – Ensures up-to-date and accurate information, filtering or correcting retrieved content before passing to the LLM. 5) Graph RAG – Converts retrieved content into a knowledge graph to capture relationships and entities. – Enhances reasoning by providing structured context alongside raw text to the LLM. 6) Hybrid RAG – Combines dense vector retrieval with graph-based retrieval in a single pipeline. – Useful when the task requires both unstructured text and structured relational data for richer answers. 7) Adaptive RAG – Dynamically decides if a query requires a simple direct retrieval or a multi-step reasoning chain. – Breaks complex queries into smaller sub-queries for better coverage and accuracy. 8) Agentic RAG – Uses AI agents with planning, reasoning (ReAct, CoT), and memory to orchestrate retrieval from multiple sources. – Best suited for complex workflows that require tool use, external APIs, or combining multiple RAG techniques. 👉 Over to you: Which RAG architecture do you use the most? _____ Share this with your network if you found this insightful ♻️ Find me → @akshay_pachaar ✔️ For more insights and tutorials on LLMs, AI Agents, and Machine Learning!

→ View original post on X — @akshay_pachaar, 2026-04-03 12:54 UTC