LLM Application Development

An LLM application is mostly the software around the model

Calling a large language model takes a few lines of code. Making that call dependable enough to sit inside a product your customers or staff rely on takes much more: getting the right context to the model, constraining what it can do, checking what comes back, recording what happened and catching regressions before users do. Tepia builds that surrounding system, which is where most of the engineering effort in an LLM application actually goes.

This page is for teams who already know the use case and need it built properly: a CTO adding an assistant to a SaaS product, an operations leader replacing a spreadsheet process with an LLM pipeline, or a product team whose prototype works in a notebook and falls apart under real traffic. If you are still choosing the use case, start with an AI readiness assessment.

The layers of a production LLM application

Tepia designs every LLM application as a set of layers, each with its own tests and owner. Not every project needs every layer at full depth, but each one should be a deliberate decision:

A batch job that classifies documents overnight needs strong validation and evaluation but little retrieval or streaming work. A customer facing assistant needs all of it. Tepia sizes each layer to the use case during Discovery.

Retrieval, tools and structured output

Three techniques do most of the work in business LLM applications, and Tepia chooses among them based on the task.

Retrieval

When answers must come from your content, such as product manuals, policies or past tickets, the application searches that content and passes the relevant passages to the model. Quality depends far more on chunking, metadata and ranking than on the model. Tepia tests retrieval on its own, measuring whether the right passage was found before judging the answer. Our RAG development page covers this in depth.

Tool calling

When the model needs live data or must take an action, Tepia exposes specific functions such as looking up an order status or creating a draft work order. Each tool has its own permission scope, and actions that change records can require a user’s confirmation in the interface.

Structured output

When the result feeds another system, the model must return data in an exact shape. Tepia defines a schema, validates every response against it, and retries or routes to a person when validation fails, so malformed output never reaches your database.

Fine tuning comes up often, and it is occasionally right for consistent formatting or a narrow classification task. For most knowledge problems Tepia reaches for retrieval first. See fine tuning vs RAG for the trade offs.

Testing and operating LLM features

Ordinary software tests check for an exact expected output. LLM output varies, so Tepia tests differently. An evaluation suite holds a few hundred graded cases drawn from real usage, each with a scoring rule: exact match for extracted fields, rubric scoring for free text, and refusal checks where the model should decline. The suite runs in CI, and a release that lowers the score does not ship.

In production, Tepia watches the things that drift:

• Model updates: providers retire and replace versions, so Tepia pins versions and reruns the suite before moving.
• Latency: long prompts and slow providers show up in tracing, with alerts when response times cross agreed limits.
• Token usage: per feature and per customer limits, caching of repeated context and smaller models for simple steps keep consumption predictable.
• User signals: thumbs down, edits and escalations are sampled weekly and turned into new test cases.

The OWASP list of LLM risks puts prompt injection first for good reason. Tepia treats any text the model reads, including documents and emails, as untrusted, and keeps sensitive tools behind permission checks the model cannot override. More detail is on our LLM evaluation and testing page.

How Tepia approaches LLM application development

Tepia’s six phases apply to LLM work with a few specific additions. A US based project manager and Tepia’s engineering leadership stay with the project from start to finish.

1. Discovery: review of your systems, data sources and access rules, interviews with the people who will use the output, and a baseline test of candidate models. Deliverables are an Investigation Summary with an architecture sketch, an Interview Summary and User Stories with acceptance criteria for each task.
2. Design: wireframes for how output, sources and confirmations appear, plus the tool permission model and output schemas.
3. Development and Testing: Alpha and Beta builds, the evaluation suite wired into CI, and test plans covering functional accuracy, user acceptance, latency and load.
4. Training: sessions for your engineers on the prompt registry, tracing and evaluation suite, and for end users on reviewing output.
5. Launch: production keys, rate limits and dashboards, migration of indexed content and a staged rollout by user group.
6. Support: model version upgrades, issue resolution, growth of the evaluation suite and feature requests.

Discovery usually spans 1 to 3 months and development and QA 2 to 6 months. Read our process or contact Tepia to talk through your architecture.

Frequently asked questions