Question 1

What is AI model fine-tuning?

Accepted Answer

Fine-tuning takes a pre-trained foundation model (like Llama 3, Mistral, or GPT) and continues training it on your specific data so it learns your domain vocabulary, tone, formats, and tasks. The result is a smaller, faster, cheaper model that often outperforms a much larger general-purpose model on your particular use case — while keeping your data and behaviour under your control.

Question 2

When should I fine-tune instead of using RAG or prompt engineering?

Accepted Answer

Start with prompt engineering and RAG — they are cheaper and faster. Fine-tune when you need: a consistent output style or format the base model struggles with, a specialized task with lots of examples, lower latency and cost at high volume, or an on-premise model for data privacy. Often the best solution combines a fine-tuned model with RAG for grounding.

Question 3

How much training data do I need to fine-tune a model?

Accepted Answer

It depends on the task. Instruction or style fine-tuning can show strong results with 500–2,000 high-quality examples. More complex domain adaptation may need 10,000+. Quality matters far more than quantity — we help you curate, clean, and format your dataset, and can generate synthetic data to fill gaps.

Question 4

Which models can you fine-tune?

Accepted Answer

We fine-tune open-source models — Llama 3, Mistral, Mixtral, Phi, Gemma, and others — using techniques like LoRA and QLoRA for efficient training. We also support managed fine-tuning on OpenAI and other hosted platforms when that fits better. We recommend the right base model and method for your accuracy, cost, and privacy requirements.

Question 5

Can you deploy the fine-tuned model in our own infrastructure?

Accepted Answer

Yes. For data privacy and cost control, we deploy fine-tuned open models in your own cloud (AWS, GCP, Azure) or on-premise using vLLM, TGI, or Ollama for serving. You own the model weights and the infrastructure — no per-token vendor fees and no data leaving your environment.

Question 6

How do you measure whether fine-tuning actually improved the model?

Accepted Answer

We build an evaluation set before training and benchmark the fine-tuned model against the base model and your current solution. We track task-specific metrics (accuracy, format adherence, latency, cost per request) and run blind quality comparisons. You get a clear before/after report so the improvement is provable, not just claimed.

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