How do I integrate an LLM into my existing software?

LLM integration is more straightforward than most developers expect on the first pass -- and more nuanced than it looks once you are in production. Here is the complete picture.

Step 1 -- Provider and Model Selection

Choose based on your use case: Claude (Anthropic) for long-context tasks and complex reasoning, GPT-4o (OpenAI) for broadest ecosystem compatibility, Gemini (Google) for Google Cloud environments. All three expose a REST API with similar shape. If data privacy requires on-premises, consider open-source models (Llama 3, Mistral) deployed on your own infrastructure -- at the cost of higher operational overhead.

Step 2 -- Build the Backend Integration Layer

• Create a service class or module that wraps all LLM API calls -- never call the API directly from route handlers
• Implement retry logic with exponential backoff for rate limit errors (429) and transient failures (500, 503)
• Add prompt versioning -- store your system prompt in a config file or database, not hardcoded in the service class
• Log every request: timestamp, model, prompt hash, token counts, latency, and a request ID you can trace back to the user action that triggered it

Step 3 -- Context Assembly

Query your existing database for the records relevant to the user's request before calling the LLM. Pass them in the system prompt or user message as structured context. Keep context lean -- send only what the model needs to answer the question, not every record in your database.

Step 4 -- Production Hardening

• Implement cost controls: log token usage per request, set spend alerts, cap context window size
• Add streaming for user-facing features where response latency is noticeable (over 2 seconds)
• Build a human review queue for high-stakes outputs (contract generation, medical advice, financial decisions)

Code and Trust integrates LLMs into existing products across all major stacks. See our AI implementation services or contact us to scope your integration.