model operations
MLOps and LLMOps Observability
An operating model for model-backed products: trace fields, evaluation checkpoints, prompt and retrieval release notes, cost visibility, latency budgets, and incident review.
- MLOps
- LLMOps
- Evals
- Tracing
Problem
Model systems fail in ways uptime checks miss. Quality regressions, retrieval drift, unsafe tool calls, rising cost, and latency spikes need reviewable traces and release context.
Approach
- Defined trace fields for user intent, retrieved context, prompt version, model choice, tool calls, policy decisions, and final output.
- Designed eval checkpoints for task success, refusal quality, regression, hallucination risk, retrieval relevance, and unsafe escalation.
- Connected model, prompt, retrieval, and policy changes to release notes so incidents can be replayed after deployment.
- Added cost and latency to the same operating view as quality and safety rather than treating them as late-stage concerns.
Artifacts
- artifactTrace review worksheet
- artifactEval failure taxonomy
- artifactModel release checklist
- artifactCost and latency dashboard outline
What this proves
- Model quality is treated as production behavior.
- Trace evidence is useful for debugging, audit, and incident review.
- LLMOps is connected to platform reliability and security controls.
Tools and surfaces
- Python
- TypeScript
- OpenAI API
- Trace tooling
- Vector search
- Dashboards
Boundary
Examples are synthetic and sanitized. No private prompts, datasets, user conversations, internal traces, or customer content are published.