Fine-Tuning Blueprint
Data Curation → Base Model Selection → Training → Evaluation → Deployment → Monitoring. End-to-end model customization pipeline.
Six stages from raw data to production model
Click any stage for technical depth.
Data Curation
Collection, cleaning, formatting, deduplication, and quality scoring.
Training data flows in from domain experts, existing logs, and synthetic generation. Each sample is validated for format compliance, deduplicated with MinHash, and scored by an LLM judge for instruction clarity and response quality. Augmentation pipelines generate edge-case variants to fill coverage gaps.
How you train determines what you get
Five approaches, each with distinct cost-quality tradeoffs. We evaluate all against your requirements.
Full Fine-Tuning
Update all model parameters on your dataset. Highest quality ceiling but requires significant compute and risks catastrophic forgetting.
Large datasets (50K+ samples), significant domain shift, dedicated infrastructure
Your data is the model
Every weakness in training data becomes a weakness in the model. Six non-negotiable quality gates.
Format Consistency
All samples follow the same schema — JSONL, ShareGPT, or Alpaca format — with no structural anomalies.
Instruction Clarity
Prompts are unambiguous, self-contained, and representative of real production queries.
Response Quality
Outputs are expert-level, factually correct, and formatted exactly as you want the model to respond.
Edge Case Coverage
Adversarial inputs, unusual formats, and boundary conditions are represented in the training set.
Category Balance
Even distribution across task types, topics, and difficulty levels to prevent model bias.
Volume Guidelines
100–10K high-quality pairs for LoRA; 10K+ for full fine-tuning. Quality always outweighs quantity.
“Garbage in, garbage out — but curated in, expert out.”
A fine-tuned model can only be as good as its training data. Invest in data curation first — it yields higher returns than any hyperparameter sweep.
Related Topics
Ready to fine-tune a model on your data?
Describe your domain and data. We'll design the training pipeline, select the base model, and deliver a production-ready custom model.