Building trust in agentic RAG starts with evidence
Basic retrieval-augmented generation (RAG) follows a straightforward pattern. A user asks a question, the system finds relevant content in a The post Building trust in agentic RAG starts with evidence appeared first on The New Stack .
Agentic retrieval-augmented generation (RAG) offers a more controlled approach to search, allowing an agent to rewrite questions and choose search methods. This can uncover evidence that traditional methods might miss. However, the increased decision-making points require clear evidence trails. Trust in agentic RAG hinges on transparency about the retrieval path and why certain sources were chosen.
Recording structured data for each retrieval step, including request, query, source IDs, scores, rejected sources, and reasons, creates a flight recorder for retrieval. Users need citations with source and passage information, while operators require detailed information like rewrite queries, search attempts, and applied filters.
This evidence should be protected with access controls, redactions, and retention rules. Claims must be supported by citations during generation, and the system should verify claim support before releasing the answer. If a claim lacks a source, it should be removed or qualified. A practical replay test involves giving an engineer the request and trace, asking them to explain the chosen source, its validity, and reasons for rejecting alternatives.
If the trace cannot answer these questions, it is not detailed enough. Semantic similarity measures indicate resemblance, not authority, so source metadata like effective date, owner, access scope, and document type should be considered during filtering and ranking. Restrictions based on source metadata, such as approval status, jurisdiction, and tenant identity, affect the retrieval process.
Written by urgent.news from The New Stack's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.
