Fine-Tuning LLMs for Production: Lessons Learned

Why Fine-Tune?

General-purpose LLMs are impressive, but they hallucinate on domain-specific tasks. Our client needed a model that could analyze legal contracts with the precision of a junior associate — not the confidence of an overenthusiastic intern.

Dataset Curation

We curated 15,000 annotated legal documents across contract types: NDAs, MSAs, SOWs, and employment agreements. Each document was annotated by practicing attorneys for key clause identification, risk scoring, and obligation extraction.

"The quality of your fine-tuned model is bounded by the quality of your training data. Garbage in, confidently wrong garbage out."

The Fine-Tuning Pipeline

We used LoRA adapters on Llama 2 70B, which gave us domain specialization without the cost of full-parameter fine-tuning.

training_args = TrainingArguments(
    per_device_train_batch_size=4,
    gradient_accumulation_steps=8,
    learning_rate=2e-5,
    lora_r=16,
    lora_alpha=32,
)

Results

• •94.2% accuracy on clause identification (vs. 71% base model)
• •3x faster contract review cycles
• •$180K/year saved in associate billable hours per client