What is LLM fine-tuning?
LLM fine-tuning adapts model behavior using curated examples. It is most useful after a baseline proves that prompting, structured outputs, retrieval, or workflow changes cannot meet the evaluation target.
LLM fine-tuning
We build the eval set, run your prompts and retrieval against it, and show you the number. If fine-tuning closes the gap, we tune. If it does not, we say so.
01
Your current model, prompt, and workflow run against representative failure cases. Prompting, structured outputs, and retrieval get tried first — they are cheaper and often enough.
02
Curated training, validation, and holdout sets, with behavioral, safety, quality, latency, and cost thresholds agreed before training starts. Licensing and retention constraints settled up front.
03
Rollback and re-evaluation criteria so the next base-model release is a decision you can make on evidence rather than a rebuild you discover the hard way.
Fine-tuning is the right move when you need stable behavior, structure, or domain performance and a labeled eval set already shows a real gap. It is the wrong move when nobody has measured yet.
Not the right call if the system primarily needs fresh facts with source citations, or prompting or structured output has not been evaluated first.
01
The target is stable behavior, structure, style, or domain performance.
02
A baseline and labeled evaluation set already show a material gap.
03
Representative training data can be used lawfully and safely.
LLM fine-tuning adapts model behavior using curated examples. It is most useful after a baseline proves that prompting, structured outputs, retrieval, or workflow changes cannot meet the evaluation target.
Fine-tuning fits stable behavioral or formatting requirements backed by enough representative training and evaluation data. RAG is usually the better tool for changing factual knowledge that must be cited.
Builderz uses three entry offers: Architecture and delivery sprint ($3K-$5K), Production build ($10K-$40K), Reliability and rescue sprint ($5K-$15K). The project brief determines which offer fits; the proposal then defines scope, owner, acceptance criteria, exclusions, payment schedule, and change control.
Builderz starts with the workflow, authority boundaries, failure modes, and acceptance tests. Reliability targets, security controls, support terms, and deployment constraints are written into the accepted scope rather than implied as blanket guarantees.
Bring the baseline model and prompt, labeled examples, target behaviors, unacceptable outputs, evaluation method, privacy constraints, inference budget, and deployment requirements.
Let's work together
Tell us what is blocked, who owns the decision, and what budget is approved. Qualified briefs get a one-business-day review.
Acceptance in writing
Criteria agreed before build starts
Proposal in 48h
After a qualified scoping call
Change control
Scope, exclusions, and owners named