AI Platforms & Models

LLM Integration

When a language model needs to sit inside an existing application, and the prototype that worked in isolation now has to handle real traffic and real failure.

Integration is where most of the engineering actually lives. A model call is trivial; a model call that handles rate limits, timeouts, partial failures, cost ceilings, and fallbacks is production software.

The design decision that ages best is keeping the choice of model separate from the application logic, so switching providers is a configuration change rather than a rewrite.

What the work includes

  • Model calls wrapped with retry, timeout, and fallback behaviour
  • Cost controls and budget ceilings enforced server-side
  • Provider abstraction so models can be swapped without rewriting callers
  • Logging appropriate for debugging without recording sensitive payloads
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// Common Questions

Questions
About This

Common questions about LLM Integration.

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Because a prototype is judged on whether it can produce a good answer, and production is judged on what happens on the bad path — timeouts, malformed output, rate limits, unavailable providers. Those cases are rare in a demo and constant at volume.

Latency budgets, retries and fallbacks, structured output validation, cost per request, and behaviour when the provider degrades. Each is straightforward in isolation; the work is in deciding what the application should do when they occur together.

By validating against a schema at the boundary and treating a failed validation as a normal outcome to handle, not an exception to log. An application that assumes well-formed output will eventually receive something else in front of a user.

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