AI development · LLM app development
LLM features that hold up on real data
LLM development is building features on large language models: chat inside your app, reading invoices and forms into clean data, sorting tickets, searching your documents, or turning text into actions through tools and APIs. We build these in your codebase with structured outputs, tests on real examples, logging and cost controls, so they behave the same in month six as on demo day.
What is included
- The feature scoped with the cases it must handle and the ones it should refuse
- Model choice by quality, speed and cost, with the option to switch later
- Structured outputs validated before your app uses them
- An evaluation set from real data, run on every change
- Logging, rate limits and a cost ceiling per user or per month
Tools we usually use
What is an LLM app?
Software where part of the work is done by a large language model: reading, writing, sorting or deciding which tool to call. The model is one component; the rest is ordinary engineering that checks its output and handles failures.
RAG, fine-tuning or just a good prompt?
Start with a clear prompt and structured output. Add retrieval (RAG) when answers must come from your documents. Fine-tune only when you need a fixed format or style at high volume and prompting is not enough. We test in that order so you do not pay for complexity you do not need.
How do you test something that is not deterministic?
With a set of real inputs and the answers you expect, scored on every change to the prompt, model or code. Failures are caught before release, not reported by users.
Questions about llm app development
Can you work in our existing codebase?+
Yes. We work in your repository through pull requests, follow your conventions and leave tests and documentation with the code.
Can the LLM run on our own servers?+
Yes, with open models when data cannot leave your infrastructure. We will tell you honestly where an open model is good enough and where it is not.
How do you control cost?+
Smaller models for simple steps, caching, limits per user and per month, and a dashboard of spend by feature.