LLM integration
Putting a language model behind a real feature, with the evaluation, guardrails and cost controls that stop it embarrassing you in production.
The problem
A prototype that works in a notebook is perhaps a tenth of the job. The rest is what happens on the inputs nobody anticipated.
What the work involves
- Provider abstraction so a model or vendor change is a config edit, not a rewrite
- Structured output with schema validation and repair, rather than parsing prose
- Evaluation harness built before the feature ships, so regressions are visible
- Token accounting and per tenant cost caps
- Prompt and response logging that survives an audit
- Graceful degradation when the provider is slow, rate limited or down
Typical stack
Claude
OpenAI
Bedrock
vLLM
Instructor
Pydantic
Laravel
Python
Indicative rather than fixed. The right choice depends on what you already run, and inheriting a stack your team knows usually beats introducing a better one nobody can maintain.
Got a llm integration problem?
Technical detail welcome. The more concrete the problem, the more useful the first reply.