Getting Started with LLM Integration

A practical guide to integrating large language models into your existing applications without the usual headaches.

Large language models are no longer a research curiosity — they’re production infrastructure. But integrating one into an existing application is where most teams hit a wall. Here’s what we’ve learned shipping LLM features across dozens of enterprise projects.

Choose the right entry point

The biggest mistake teams make is treating LLM integration as a search-and-replace for business logic. It’s not. Start by identifying tasks that are:

Unstructured by nature — summarisation, classification, extraction from free text
High-value but low-volume — where the cost per call is justified by the outcome
Tolerant of occasional errors — always have a fallback path

Avoid LLMs for anything that needs deterministic output or where latency is under 100ms.

Structure your prompts like code

Prompts are code. Version them, review them, test them. A prompt that works in January may degrade after a model update. Keep prompts in version control alongside the logic that calls them, and write eval suites that run on every deploy.

You are a contract analyst. Extract the following fields from the document:
- Party names
- Effective date
- Termination clauses

Return as JSON. If a field is absent, return null.

Handle failure gracefully

LLMs fail in novel ways: timeouts, rate limits, hallucinated JSON that breaks your parser. Design your integration to degrade gracefully:

Set explicit timeouts — don’t let a slow inference call block your request lifecycle
Validate structured outputs with a schema before trusting them
Log inputs and outputs for every call — debugging without traces is guesswork

Watch your costs

Token costs compound quickly at scale. A few practical controls:

Cache responses for identical or near-identical inputs
Truncate context aggressively — most prompts carry far more than the model needs
Use a smaller model for classification and routing; reserve the large model for generation

What’s next

Once the basics are stable, the interesting work begins: retrieval-augmented generation, tool use, multi-step agents. But those are only worth building on a solid foundation.