Series

AI-Powered Backend Services

LLM APIs don't fail the way REST APIs do. A 200 response with a well-formed body can still contain output you can't use, costs you didn't expect, or behaviour that changed silently after a model update. This series covers building AI-powered backend services that hold up in production — prompt management, output validation, async pipelines, cost control, testing non-deterministic code, and deployment patterns for variable-latency workloads. Written from real experience integrating OpenAI and Gemini into production FastAPI services on GCP.

Building AI-Powered Backend Services: Why Most Integrations Break in Production
The integration looked solid. In testing, the AI-powered document extraction service worked exactly as designed — pull a PDF, send it to the model, parse the structured output, write to the database.
Aug 22, 20264 min read

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