Self‑RAG

Add a reflect‑then‑retrieve loop: produce a draft, detect missing facts, retrieve extra context, and revise with citations.

What Self-RAG Means for Your Business

Self-RAG reduces costly mistakes by double-checking information before responding. Critical for businesses where wrong answers hurt trust or compliance.

The Self-Correcting AI That Protects Your Business

How It Works

Self-RAG doesn't just answer — it double-checks itself by reflecting on its response, identifying gaps, and retrieving additional information to ensure accuracy.

  • • Generates initial answer
  • • Self-assesses confidence level
  • • Identifies missing information
  • • Retrieves additional context
  • • Revises with corrections

Real Business Example

Financial Advisory Service:

A financial advisor's AI says "Our minimum investment is $10,000" then double-checks recent policy updates and corrects itself: "Actually, we lowered it to $5,000 last month."

Result: Prevents costly compliance violations and maintains client trust through accurate, up-to-date information.

Bottom Line: Self-RAG is essential for businesses in regulated industries, financial services, healthcare, or any field where accuracy directly impacts customer trust and legal compliance.

Draft & reflect
Generate an initial answer and self‑assess confidence and missing evidence.
Targeted re‑retrieval
Issue follow‑up queries for specific gaps identified by the reflection.
Revise with citations
Incorporate new evidence and cite sources explicitly.
Stop conditions
Limit loops by confidence thresholds, token budgets, or max rounds.

Reduce omissions and improve faithfulness

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