How do you build serverless generative AI on AWS?

Separate model inference from application control

An API or event accepts a request, validates identity and limits, retrieves allowed context, invokes a selected model, validates the response, and stores or streams the result. Longer tasks can use queues and workflows so clients do not hold one fragile synchronous connection.

Keep authoritative state in databases or object storage rather than model context alone. Prompts, model identifiers, inference parameters, retrieved sources, tool results, and policy decisions should be versioned enough to investigate behavior safely.

Ground responses and bound every tool

Retrieval can provide relevant enterprise context, but retrieved text is untrusted input and may be stale, unauthorized, or malicious. Filter by caller access, preserve source metadata, and distinguish source evidence from model-generated claims.

Expose narrow tools with typed inputs, validation, least-privilege roles, idempotency, timeout, cost limits, and audit records. Require human approval for high-impact, irreversible, or financially material actions.

Evaluate quality, safety, latency, and cost together

Create representative evaluation sets for factuality, task completion, refusal, security boundaries, harmful output, prompt injection, tool selection, and edge cases. Run them when prompts, models, retrieval, policies, or tools change.

Track model and application latency, token or request usage, retrieval quality, tool failures, retries, user feedback, and business outcomes. A syntactically successful model call is not proof that the task was completed correctly.

Design quotas, failure, privacy, and recovery

Bound input and output size, concurrency, model rate, workflow duration, tool calls, and per-request spend. Use backpressure and fallback behavior when a model, region, retrieval source, or tool is unavailable.

Minimize sensitive data, define retention, encrypt data, prevent secrets from entering prompts, and control who can inspect traces and evaluations. Provide cancellation, replay, manual completion, and incident procedures for durable AI workflows.