Self-RAG: Retrieve, Generate, Critique for Higher Factuality

Self-RAG trains or prompts an LM to decide when to retrieve and to self-critique its outputs, improving factuality and control.

TL;DR

Self-RAG lets the model decide when to retrieve and when to critique its own outputs, reducing hallucinations via explicit reflection and control tokens/prompting.

What Is Self-RAG?

A single LM (or orchestrated prompts) that interleaves retrieval and self-critique using special tokens or structured steps, adapting to the query on the fly.

When to Use Self-RAG

Factual report writing and long-form educational content
Systems needing explicit citation checks
Iterative summarization and refinement tasks

Simulating Self-RAG in N8N

Generate an initial answer (OpenAI node)
Run a critique step: ‘Is the answer fully supported by sources?’
If low confidence, trigger additional retrieval and regenerate
Optionally mark segments with <retrieve>/<critique> style tokens

Strengths & Weaknesses

Strengths: adaptive retrieval and self-correction; improved factuality and controllable behavior. Weaknesses: complex design; may require custom training; higher latency and orchestration cost.

Metrics to Track

Factuality scores and citation precision
Answer accuracy vs baseline RAG
Additional latency/cost from critique steps