Integrate AI Solutions with Data and Services
Integration is where AI solutions meet reality: data sources, APIs, identity, and failure modes. For retrieval-augmented generation, understand the pipeline - ingestion, chunking, embedding, indexing, retrieval, reranking, prompt assembly. Chunk size and overlap affect recall and precision; metadata filters scope retrieval to the right tenant or document class. Evaluate retrieval separately from generation, because a correct answer from wrong context is still a defect.
For service integration, know authentication patterns (managed identity or scoped keys over embedded secrets), rate limiting with retry and exponential backoff, timeouts, and circuit breaking. Design for partial failure: a downstream API outage should degrade gracefully, not crash the workflow. Normalize and validate data at boundaries; treat model output as untrusted input to downstream systems.
Structured output (JSON schemas, typed responses) makes model output machine-consumable and testable. Prefer schema-constrained output over parsing free text.
Traps:
- Storing credentials in prompts or client code.
- Assuming retrieval returns the right document without metadata scoping (cross-tenant leakage).
- Retrying non-idempotent writes and duplicating side effects.
- Passing raw model text into a database or API without validation.
Rule of thumb: validate at every boundary - data in, model out, and service responses. Worked example: a document Q&A service should filter the index by tenant and access level before retrieval, rerank the top candidates, require the model to cite chunk IDs, and reject answers whose citations fall outside the retrieved set.