Getting Started with LLM Integration in Your Enterprise
Every enterprise we talk to is asking the same question: where do we start with LLMs? The good news is that you don’t need a research lab or a fine-tuned model to deliver something valuable. Here’s the roadmap we use with our clients.
Start with a narrow, high-friction task
The best first LLM feature is one where:
- Users already do the task manually and find it tedious
- The output is easy to verify at a glance
- A wrong answer is annoying, not catastrophic
Summarizing support tickets, drafting replies, and extracting structured data from contracts and invoices are all classic first wins. “An AI that runs my whole business” is not.
Keep the model at arm’s length
Treat the LLM as an unreliable but talented intern. In practice that means:
const response = await client.messages.create({
model: 'claude-sonnet-5',
max_tokens: 1024,
messages: [{ role: 'user', content: prompt }],
});
// Always validate before you trust
const parsed = ticketSummarySchema.safeParse(extractJson(response));
if (!parsed.success) return fallbackSummary(ticket);Validate every output against a schema, design a graceful fallback, and log everything. Your future self will thank you when you’re debugging why quality dipped last Tuesday.
Measure before you optimize
Before reaching for fine-tuning or exotic architectures, build a small evaluation set: 50–100 real examples with known-good outputs. Run it on every prompt change. Most teams discover that better prompts and better retrieval beat a bigger model — at a fraction of the cost.
Ship, watch, iterate
LLM features are never “done” — usage reveals cases no eval set predicted. Ship behind a flag, watch real interactions, and iterate weekly. Teams that treat the first release as the start of the process, not the end, are the ones whose AI features users actually keep using.
Thinking about your first (or next) LLM initiative? We’d love to help.