Frontier AI, Frontier Engine Math, & New Models
Self-Correcting Agentic RAG
Agentic RAG lets an agent plan retrieval, inspect results, and retry or refine queries instead of one-shot retrieval. Self-correction improves accuracy on multi-hop questions but adds steps, cost, and failure modes. The discipline is bounding the loop and measuring end-to-end success.
Points clés
- Plan retrieval as steps: decompose the question, retrieve, then decide if more is needed.
- Use a critic or verifier to check whether retrieved context actually answers the question.
- Retry with rewritten queries when results are off-topic or insufficient.
- Cap steps, tokens, and tool calls to prevent runaway loops and cost.
- Keep a simple single-pass RAG baseline to compare against the agent.
- Log every retrieval decision and result for debugging and evals.
- Evaluate on multi-hop and adversarial questions, not just single-fact lookups.
- Add human fallback for cases where the agent cannot reach a confident answer.
Questions fréquentes
Is agentic RAG always better than standard RAG?
No. It helps on multi-hop and ambiguous queries but adds cost and latency; many tasks need only one pass.
How do I stop runaway loops?
Set hard limits on steps, tokens, and retries, and require a stopping condition.
What makes self-correction fail?
Bad retrieval, weak verification, and agents that confirm their own wrong answers.
How do I evaluate it?
Measure end-to-end answer accuracy and cost per correct answer against a baseline.
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