A language model is the easy part. Everything around it decides whether the feature is useful: what you retrieve, how you prompt, what you do when the answer is wrong, and what the whole thing costs per thousand users.
Retrieval before prompting
Most bad answers are retrieval failures wearing a prompt costume. We start with your content — documents, tickets, product data — and get the chunking, the index and the ranking right before anyone tunes wording. A model can only be as correct as the passages you hand it.
Evaluation is the deliverable
We build an evaluation set from real questions your users ask, with graded answers, and we run it on every change. That set is yours. It is the only honest way to say a change made the feature better, and it is what turns a demo into something you can maintain after we leave.
Cost and latency are product decisions
Model choice, context length, caching and routing all move the bill and the wait. We size those against the job. A cheap model with good retrieval usually beats an expensive one guessing from a long context.
Guardrails you can explain
- Grounded answers with citations back to the source passage
- A refusal path when confidence is low, instead of a fluent invention
- Input and output filtering on anything user-facing
- Logging that lets you replay any answer the system gave
Where it lives in the stack
LLM features sit inside an existing product. That means real integrations and API work, real QA and testing and a support plan. We do not hand over a notebook.
What you get
A shipped feature, the evaluation set behind it, a cost model, and a runbook for the day the provider changes something. If the work needs to act rather than answer, read agentic AI development next, or go back to the AI map.