LLM fine-tuning

LLM fine-tuning, when it actually beats retrieval

Fine-tuning is the right tool for a narrower set of problems than most teams assume a high-volume, well-defined task where a smaller tuned model can match a larger general model at a fraction of the cost, or a style/format the base model won't reliably hold. We'll tell you honestly if your use case needs it, or if retrieval solves it faster and cheaper.

Domain-tuned modelsCost optimizationRAG vs. fine-tuning
Quick answer

LLM fine-tuning services for domain-specific business data and honest guidance on when fine-tuning beats retrieval, and when it doesn't.

Fine-tuning is often the wrong first move

Teams reach for fine-tuning because it sounds like the 'serious' AI investment, then spend weeks curating training data for a problem retrieval-augmented generation would have solved in days, at lower ongoing cost, and without the maintenance burden of a custom model that needs retraining every time the underlying knowledge changes.

What we deliver

  • An honest RAG-vs-fine-tuning assessment before any training data is curated
  • Training data curation and evaluation set construction from your real examples
  • Fine-tuned models for high-volume, narrowly-scoped tasks where the economics justify it
  • Cost and latency benchmarking against the general-model + RAG alternative
  • Retraining pipelines so a tuned model stays current as your data evolves

How we work

  1. Start with the RAG-vs-fine-tuning question, not the fine-tuning job itself

  2. Curate training data from real production examples, not synthetic data alone

  3. Build the evaluation set first, so improvement is measured, not assumed

  4. Benchmark cost and latency against the alternative you didn't choose, to confirm the decision

Typical stack

Anthropic / OpenAI fine-tuning APIsOpen-weight models (Llama)Evaluation frameworksPython

Frequently asked questions

When you have a high-volume, well-defined task where a smaller fine-tuned model can match a larger general model at meaningfully lower cost, or when you need the model to reliably hold a specific style, format, or behavior that prompting alone won't enforce. If the task is about knowing facts, RAG is almost always the better first move.

It varies by task, but useful fine-tuning often starts in the hundreds to low thousands of high-quality examples, not the millions people assume. Quality and consistency of the examples matters more than raw volume a smaller, carefully curated set usually beats a larger noisy one.

If the underlying knowledge changes pricing, policies, product catalog yes, and that retraining cost is the main reason we check the RAG alternative first. RAG updates by re-indexing data; a fine-tuned model needs a new training run.

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