LLM Eval-in-CI
Drops LLM output evaluation into your CI pipeline so every commit that touches a prompt, model, or RAG config gets scored against a fixed test set, and the build fails if quality regresses.
- Target market
- Indie devs and 2-15 person teams shipping an LLM feature on GitHub Actions who cannot justify a $500/mo eval platform.
Problem snapshot
What this solves
Most indie AI products have zero automated quality checks. A dev tweaks a prompt to fix one edge case, ships it, and silently breaks ten others; nobody notices until a user complains. The enterprise eval platforms are priced and scoped for funded teams, so solos just eyeball a few outputs and hope for the best.
Full ProvenTools analysis
Unlock the full analysis and build prompt
Unlock the solution, revenue model, feature scope, technical approach, user flow, and 13-section AI build prompt.
Already have access? Sign in
Related ideas
Explore similar problems
Affiliate Attribution Drop Finder
A detector that checks whether affiliate links, landing pages, checkout redirects, cookies, and post-purchase events still preserve attribution.
Agent Action Approval Gate
A runtime approval gate that intercepts only high-risk agent actions, such as refunds, deployments, deletes, emails, and database writes, before execution.
Agent Data-Access Scoper
A Supabase and Postgres RLS test harness that proves which rows an agent role can read or write before the agent is released.