Retrieval-augmented generation
AI answers grounded in approved internal information.
Explore Retrieval-augmented generationAI architecture
LLMOps & evaluation
Maintain AI applications with versioned prompts, representative evaluations, and observable execution.
Discuss your projectA prompt change deserves a release decision
Illustrative release rubric — no measured scores
| Dimension | Evidence required | Release question |
|---|---|---|
| Task correctness | Reviewed expected outcomes | Does the workflow still do the right job? |
| Unsupported claims | Source and answer review | Does it acknowledge missing evidence? |
| Tool behavior | Action traces and authorization cases | Are actions permitted and recoverable? |
| Latency and cost | Representative workload runs | Does it fit the operating budget? |
Evaluation makes changes reviewable. Keep the dataset, prompt, model configuration, and results together so an improvement on one task does not conceal a regression on another.
Build the dataset from actual failure modes
Ordinary work
Include representative requests with reviewed outcomes. Avoid a test set made only of unusually clean or easy examples.
Boundary cases
Include missing data, conflicting instructions, restricted records, and malformed tool outputs. Specify what a safe and useful response should be.
Production feedback
Convert confirmed failures into regression cases after removing unnecessary sensitive data. Keep an owner for reviewing and updating expected outcomes.
Your project, scoped around the outcome
Build with LLMOps & evaluation
Versioned evaluation cases, regression reports, and monitoring for quality and cost.
Talk through the scopeRelated technologies
Tools, agents & orchestration
AI-assisted workflows that can request business-system actions.
Explore Tools, agents & orchestrationInference & cost engineering
AI workflows with demanding latency or usage constraints.
Explore Inference & cost engineering