Summary
Guide users towards highest-impact improvements by suggesting concrete actions after eval, regression failure, or incident review. Examples: change chunk size, add benchmark case, update policy, tune prompt, adjust fallback, tighten tool scope.
Problem to solve
Detection without action is common in ML ops. Users receive pass/fail results but lack expert guidance for improvement. Product value is maximized by bridging the gap from detection to remediation.
Proposed solution
- On each eval/incident completion, provide targeted suggestions (with rationale and options)
- Suggest actions on data, prompt, policy, retriever, or model side
- (Optionally) Integrate with PR regression gate and incident review
Alternatives considered
- Users research on their own; rely on best practices or vendor consulting
- External playbooks and workaround docs
Who benefits?
Priority / impact
- Turns platform from insights to improvement
- Shortens iteration loops and enhances perceived value
Additional context
Inspiration: Model monitoring products with built-in recommendations, AI-generated pull request suggestions, lint/fix tools in ML pipelines.
Summary
Guide users towards highest-impact improvements by suggesting concrete actions after eval, regression failure, or incident review. Examples: change chunk size, add benchmark case, update policy, tune prompt, adjust fallback, tighten tool scope.
Problem to solve
Detection without action is common in ML ops. Users receive pass/fail results but lack expert guidance for improvement. Product value is maximized by bridging the gap from detection to remediation.
Proposed solution
Alternatives considered
Who benefits?
Priority / impact
Additional context
Inspiration: Model monitoring products with built-in recommendations, AI-generated pull request suggestions, lint/fix tools in ML pipelines.