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.
Model adaptation
Builderz starts with representative evaluation cases and compares prompting, structured outputs, retrieval, and workflow changes. Fine-tuning proceeds only when it has a defined behavioral target.
Baseline model, prompt, workflow, and measured failure cases.
Curated training, validation, and holdout examples.
Behavioral, safety, quality, latency, and cost thresholds.
Privacy, licensing, retention, and deployment constraints.
Rollback and re-evaluation rules for future model changes.
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.