infographics: AI RAG evolution

 Post | LinkedIn

Based on the LinkedIn post by Raju Dandigam, the infographic illustrates the chronological evolution of Retrieval-Augmented Generation (RAG) across four major stages from 2020 to 2025.

Here is a breakdown of what each stage depicts in the visual:

1. Naive RAG (2020)

• The Flow: A user asks a question, the system runs a single vector search against a vector database, and the retrieved chunks are sent directly to the LLM to generate an answer.
  
  
• The Core Limitation: Relies entirely on basic vector similarity without checks, meaning "weak chunks in = weak answer out."
  
  

2. Advanced RAG (2023)

• The Flow: The user's question first goes through a query rewriting step. The system then performs a hybrid search (combining vector similarity and keyword retrieval) before passing the results through a reranker to keep only the highest-quality chunks.
  
  
• The Improvement: Significantly improves retrieval precision and the final answer quality.
  
  

3. GraphRAG (2024)

• The Flow: Documents are processed to convert information into entities and relationships, building a knowledge graph.
  
  
• The Improvement: Uses community summaries to help answer complex, multi-document, and broad big-picture questions that standard chunking misses.
  
  

4. Agentic RAG (2025)

• The Flow: Introduces an AI agent into the loop that dynamically plans, retrieves, verifies results, and repeats as needed. It can access a mix of vector databases, knowledge graphs, web/API search, and SQL databases.
  
  
• The Improvement: Shifts the architecture from static retrieval pipelines to adaptive, reasoning-driven systems capable of multi-step problem-solving.