SAG / ARCHITECTURE NOTE
CRAG: How to Correct the Answer Path When Search Results Are Poor
Explains what CRAG is, why it is needed, how it works, and how to apply it within the SAG architecture, with practical checklists and support from research and official documentation.
One-sentence definition
CRAG is a corrective RAG approach that evaluates the quality of search results and, when that quality is low, changes or supplements the search scope.
Key answer: RAG can produce plausible but incorrect answers when retrieved documents are wrong or irrelevant. A control path that does not blindly trust search results is necessary.
Why is this technology needed?
RAG can produce plausible but incorrect answers when retrieved documents are wrong or irrelevant. A control path that does not blindly trust search results is necessary.
How it works
A retrieval evaluator classifies results as correct, ambiguous, or incorrect, then selects another path, such as knowledge refinement or web search. Evaluator errors and the reliability of external search must also be managed separately.
Design involves more than accuracy. Latency, cost, data boundaries, refresh cycles, and behavior on failure must also be defined to produce reproducible results in production. For values that automation cannot determine with confidence, it is safer to leave them in an unmeasured or review-required state rather than change them to 0 or mark them as successful.
Connection to SAG technology
SAG’s handling of task states and missing evidence is aligned with the operational principle of not disguising low quality as success. Actual external search integration is treated separately from the current implementation scope.
Practical checklist
- Represent insufficient evidence as an explicit state
- Apply source policies to each corrective path
- Evaluate accuracy and cost together, before and after correction
- Distinguish the states for failures, empty results, and permission errors from success
- Revalidate under the same conditions before and after changes
Research and official documentation
Reference documents provide support for principles and recommendations. They do not guarantee search visibility, AI mentions, rankings, or revenue; the actual effects of implementation must be verified through observations of service data under the same conditions.
Selection criteria and a concrete application example
If the retrieved document describes product B instead of product A, search quality should be evaluated and a corrective path selected before generation. Results broadened through web search must also be checked again for official status, date, and original source; correction does not always improve accuracy.
Scope of application at SAG
This article covers research principles in search AI and extension design. Read it in connection with SAG’s page collection, evidence recording, and report validation structure, but do not interpret it to mean that all search algorithms from the paper have been incorporated into the production pipeline. Whether they have been applied should be confirmed through the search module, evaluation data, and execution records.
How to continue reading about this technology
Compare RAG, GraphRAG, and Self-RAG papers and their conditions for application.
SAG / KNOWLEDGE LINKS