Adding LLM features to a product you already have

How do you add an LLM feature to an existing product?

Pick one narrow task where a wrong answer is cheap to catch, connect the model through your existing backend, and measure its output on real examples before any user sees it. Start small, keep a human in the loop where the stakes are high, and expand only once the numbers hold up.

Choose the first feature carefully

The best first feature is boring and specific. Good candidates share three traits: the input is text you already have, a person currently does the task by hand, and checking the result is quicker than doing the task from scratch.

Examples that fit:

• Summarizing long support tickets or call notes for a team lead
• Drafting a reply that a person reviews before sending
• Pulling fields out of documents into a form
• Tagging and routing incoming requests

Examples that need more care: anything that acts on its own, touches money, or gives advice in a regulated area.

Connect it through your backend

Keep the model call on the server. That keeps keys out of the browser, lets you log requests without exposing user data, and gives you one place to add limits and fallbacks. Treat the model like any other third-party service: set timeouts, handle failures and make sure the product still works when it's slow or down.

Pass the model only what it needs. Sending less data lowers cost, lowers risk and often improves the answer.

Test it before users see it

You can't judge an LLM feature by trying a few prompts and liking the results. Build a small set of real examples with the answer you'd expect, and run every change against it.

1. Collect 30 to 50 real inputs, including awkward ones.
2. Write down what a good output looks like for each.
3. Score outputs against that list, by hand at first and with automated checks where you can.
4. Re-run the set whenever you change the prompt, the model or the data you pass in.

This is what lets you swap models later without guessing.

Guardrails that matter

• Validate the output. If you expect JSON or a fixed set of labels, check the response and retry or fall back when it doesn't match.
• Limit what it can do. Give the feature read access only unless an action is clearly safe.
• Keep a person in the loop for anything customers see or anything hard to undo.
• Plan for wrong answers. Show users where the text came from and make it easy to correct.
• Keep personal data out of logs.

Control the cost

Costs come from the amount of text going in and out, and from how often you call the model. Trim the input, cache answers that repeat, use a smaller model for easy steps, and set a spending limit with an alert. Track cost per use of the feature so you can see whether it pays for itself.

Where this fits

Machine learning doesn't have to mean a chatbot. In a job-matching platform we replaced fixed SQL queries with a trained model and cut database cost by moving older data to cloud storage. The same discipline applies to LLMs: pick the task, measure the output, watch the cost.

If you're weighing a first AI feature, our AI apps and automation service page covers what we build, and a discovery call is a good place to test the idea.