Adding AI to your product changes more than one API call
An AI application adds data, context retrieval, limits, evaluation and entirely new failure modes.
From request to workflow
A traditional function often has predictable inputs and outputs. An AI function may select context, query a vector database, invoke tools, retry a model and validate its response. Every step adds latency, cost and security surface.
The production pieces
- Gateway and authentication: who can use the capability and within which limits.
- Data and RAG: ingestion, indexes, document-level permissions and knowledge freshness.
- Evaluation: quality, safety and regressions before prompts or models change.
- Observability: traces connecting models, retrieval, tools, latency and tokens.
Design for imperfect responses
Timeouts, quotas, blocked content and invalid responses belong in the normal flow. Build bounded retries, graceful degradation, caching where useful and human review for sensitive actions.
Keep a useful boundary
Separate application logic, orchestration and model provider. That boundary lets you compare models and costs without rewriting the full product.
Sources reviewed
This article is an original Nubent synthesis. The sources let you inspect the basis and explore each subject further.