Ai Llm Integration In Fullstack Apps
Use When
- Product adds AI-assisted features
- Need safe and measurable AI behavior
Workflow
- Define prompt templates and versioning
- Implement input/output guardrails
- Add tracing for latency, cost, and quality
- Handle fallback when model unavailable
- Review data residency and PII policies
Required Checks
- Prompt injection risks mitigated
- Cost budgets and rate limits configured
- Human escalation path for high-risk outputs
- Evaluation set tracks quality regressions
Decision Framework
- Prefer explicit contracts and compatibility rules before implementation.
- If dependency risk is high, require timeout, retry, and fallback strategy per call path.
- If async messaging is used, require idempotency, replay, and dead-letter handling.
- If traffic patterns are volatile, require load, failover, and scaling validation before ship.
Common Rationalizations And Rebuttals
- "Retries will handle failures automatically." -> Unbounded retries can amplify outages; use budgets.
- "We can skip runbooks for now." -> Operational ambiguity delays incident recovery.
- "Contract changes are minor." -> Small breaking changes cause broad downstream regressions.
Evidence Pack
- Contract compatibility note and migration strategy (if applicable)
- Failure-mode test evidence for dependency degradation and recovery
- Observability snapshot (latency, error, saturation, or queue health)
- Rollout and rollback steps with clear trigger thresholds
Exit Criteria
- Workflow is production-ready with verified evidence
- Operational and security guardrails are in place
- Release and rollback expectations are documented
