CI/CD for AI Apps implementation checklist

Store prompt templates in dedicated JSON or YAML files separate from application logic to enable independent versioning and testing.

Run JSON schema validation against prompt templates in the CI pipeline to ensure all required variables are present before deployment.

Hardcode specific model versions (e.g., gpt-4-0613) in configuration files rather than using 'latest' aliases to prevent unexpected drift during deployments.

Configure the CI pipeline to generate and display a diff of prompt changes in pull request comments for manual reviewer audit.

Inject the Git commit SHA and environment name into the metadata of all LLM API calls for downstream traceability.

Use mocks for LLM API calls in unit tests to verify that application logic handles various response formats and error codes without incurring costs.

For prompts intended to return JSON, execute a validation step in CI that parses the response and checks for required fields and data types.

Run an evaluation script comparing LLM outputs against a 'golden set' of expected answers using cosine similarity or LLM-as-a-judge metrics.

Maintain a dataset of 50-100 edge-case prompts that must be executed and validated whenever the model version or system prompt changes.

Integrate a security scanner in the pipeline to test if system prompts can be bypassed via malicious user input patterns.

Store LLM API keys in a secure vault (e.g., GitHub Secrets, HashiCorp Vault) and ensure they are never logged in CI build traces.

Implement a dedicated /health/ai endpoint that verifies connectivity to upstream AI providers and model availability before routing traffic.

Configure the deployment pipeline to route a small percentage of traffic to new prompt versions before full cutover.

Define automated rollback triggers based on a spike in 4xx/5xx errors or a drop in semantic similarity scores post-deployment.

Measure and log the p95 latency of model responses during integration tests to prevent performance regressions from reaching production.

Set hard limits on the number of tokens or API spend allowed per CI pipeline run to prevent runaway costs from recursive loops or large test suites.

Distribute LLM evaluation tasks across multiple parallel CI jobs to reduce build times when running large-scale semantic tests.

If self-hosting models, use persistent volume claims or CI caching layers for model weights to avoid multi-gigabyte downloads on every build.

Configure CI to only run expensive LLM evaluation suites if changes are detected in prompt files or model configurations.

Use smaller, cheaper models (e.g., GPT-3.5 or Haiku) for basic logic testing in CI, reserving larger models for final staging evaluations.

Ensure the CI/CD pipeline verifies that OpenTelemetry spans are correctly configured for all LLM calls to enable production tracing.

Run a test case in CI that attempts to log PII through the AI feature and verifies that the logging middleware correctly redacts the data.

Automate the deployment of Grafana or Datadog dashboards alongside the application to track token usage and error rates per model.

Verify that the production environment is configured to capture and store user 'thumbs up/down' feedback linked to specific prompt versions.

Verify that deployment scripts update alerting thresholds for inference failures and token exhaustion in the monitoring provider.