Self-Reflective RAG — Agentic RAG

[ ABORT TO HUD ]
SEQ. 1
SEQ. 2
SEQ. 3
SEQ. 4
SEQ. 5

Self-Reflective RAG

📚 Agentic RAG⏱ 12 min⭐ 80 BASE XP⌨ HANDS-ON LAB

Agentic RAG

Instead of a linear pipeline, we can use an Agent to handle retrieval dynamically.

An Agentic RAG system can:

  • Query Reformulation: The agent rewrites the user's messy question into a clean search query.
  • Self-Critique: The agent gets the search results and asks: "Did this actually answer the question?"
  • Multi-Hop: If it didn't find the answer, it searches again with a different query.
🎯 Pro Tip: GraphRAG is an emerging pattern where documents are converted into a Knowledge Graph (Entities and Relationships) before searching. It excels at answering global questions like 'what is the overall theme of these 10 books?'
⌨ HANDS-ON LABImplement Self-RAG Retrieval Grader
⭐ +150 XP

Construct an LLM evaluation node that grades retrieved passages for relevance and triggers automated query rewriting if quality is low.

1Simulate Self-RAG retrieval grading and query rewrite.
lab-sandbox — simulated environment
INFINITY LAB SANDBOX v2.6 — simulated shell
Type the command for the current objective. Helpers: "hint", "solution", "clear".
$
OBJECTIVE 1 / 1 — type "hint" if stuck
SYNAPSE VERIFICATION
QUERY 1 // 1
How does Agentic RAG differ from standard RAG?
It uses image models
It actively decides when to search, reformulates queries, and verifies if the retrieved context is sufficient
It doesn't use vector databases
It trains a new model locally