LLM fine-tuning and prompting
Tune large language models to your voice, format and domain, the efficient way.
We make large language models (LLMs) reliable for your use case through structured prompt engineering and, where it pays off, fine-tuning. We start with prompting, few-shot examples and retrieval because they are cheaper to change, then fine-tune (often parameter-efficient methods such as LoRA) when you need a consistent style, format or domain skill. Every change is measured against an evaluation set, so improvements are proven, not assumed.
Included in this service
Prompt engineering
Structured system prompts, few-shot examples and output schemas that make responses consistent and parseable.
Parameter-efficient fine-tuning
LoRA and similar methods to teach style, format or domain knowledge without the cost of full retraining.
Dataset curation
We build and clean the instruction or preference dataset that fine-tuning actually depends on.
Before-and-after evaluation
A held-out test set proves the tuned model beats the prompt-only baseline before it ships.
What you walk away with
- Hardened prompt templates with output schemas
- Fine-tuned model adapter (where justified)
- Curated training and evaluation datasets
- Comparison report: prompting vs fine-tuned
- Cost and latency tuning
- Retraining or re-indexing routine
A clear path from problem to outcome
Frame and qualify
We pin down the business outcome, the data you already hold and the constraints (latency, budget, privacy, residency). We agree success metrics up front, an offline accuracy or quality target plus a business KPI, so we build something measurable, not a demo.
Prototype on your data
We build a working proof of concept against a representative slice of your real data, not a public dataset. You see honest numbers early: retrieval quality, model accuracy, cost per request and failure cases, so the go or no-go decision is evidence based.
Engineer for production
We harden the prototype into a reliable system: data and feature pipelines, evaluation suites, access controls, observability and CI/CD. We integrate with your stack and put guardrails and human review where the cost of an error is real.
Deploy, monitor and improve
We ship to production, instrument it and watch for drift, regressions and cost creep. You get clear documentation, a retraining or re-indexing routine and an evaluation baseline so quality holds up and you can improve it over time.
Frequently asked questions
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