Common Questions About LLM Fine-Tuning

It depends on your data volume and complexity, but most projects take 4–8 weeks from data collection to deployment. The first 1–2 weeks usually goes into understanding your domain, gathering and cleaning data, and selecting the right base model. Actual fine-tuning can happen in days, but validation and performance testing add the real time investment.

Yes, but we take data governance seriously. We work under strict confidentiality agreements, and your data never leaves a secure environment. Many clients keep their data on-premises or in a private cloud instance, and we can work with you on infrastructure that fits your compliance requirements—whether that's healthcare regulations, legal privilege, or trade secrets.

Prompt engineering gets you part of the way there—it's quick and cheap. But fine-tuning actually rewires the model's understanding of your domain's terminology, workflows, and edge cases. If you're asking it the same questions repeatedly or dealing with specialized language, fine-tuning usually beats prompts on accuracy and cost-per-use over time.

There's no magic number, but you'll want at least 200–500 quality examples for meaningful results. We usually recommend starting an audit of what you already have—past project documents, case studies, training materials, customer interactions. Most organizations have more than they think. Quality matters more than quantity, so clean, representative examples beat thousands of messy ones.

That depends on your setup. We can deploy it as an API you call from your existing tools, embed it in a web app, or run it locally on your infrastructure. We'll help you decide what makes sense based on your usage patterns, compliance needs, and budget. Most clients integrate it into their workflows within a few weeks of deployment.

We run your model against a test set you approve—questions and answers that represent real-world usage. You'll see metrics like accuracy on domain-specific tasks, latency, and cost. But more importantly, you'll run it yourself and see if it's actually giving you better answers than the base model. We usually build in a feedback loop so you can tell us what's working and what isn't.