PROBLEM
Why this system exists
Non-deterministic AI systems need repeatable quality controls and objective release evidence rather than subjective prompt demos.
LLM Quality & Release Engineering
Deployed
LLM EvalOps & Release Evidence Platform
Deterministic evaluation, RAG quality, safety red-team testing, regression gates, and fail-closed release evidence.
A production-style Python evaluation platform with provider abstraction, JSONL datasets, prompt boundaries, model comparison, RAG metrics, safety checks, experiment tracking, model aliases, CI gates, and a public release dashboard.
OUTCOME
What the build proves
Separates a passing release candidate from a controlled blocked-release scenario and proves that missing retrieval evidence prevents promotion.
IMPLEMENTATION PROOF
Evidence a technical reviewer can inspect.
A public application, service, or dashboard is available. The label describes deployment status, not enterprise production scale.
Built capabilities
- Automated regression, RAG, safety, and release-readiness gates
- Controlled failure exits non-zero when required retrieval evidence is missing
- Public dashboard distinguishes ready and blocked release evidence
Technology stack
PythonPytestRAG EvaluationGitHub ActionsOpenAI AdapterJSONLRed TeamingStatic Dashboard
Evidence available
ENGINEERING BOUNDARIES
Precise claims build trust.
- The public deployment is a static evidence dashboard, not a claim of enterprise production traffic.
- Deterministic providers support safe CI; live provider execution requires separately configured credentials.
RECRUITER / HIRING MANAGER
Need the architecture walkthrough?
I can explain the design decisions, implementation evidence, tradeoffs, and production-hardening path in a focused technical review.