Fine-Tuning LLMs on Enterprise Data

General-purpose LLMs are remarkably capable, but they often fall short on domain-specific tasks. Fine-tuning bridges that gap by adapting a pre-trained model to your specific data and use case. Here is how we approach fine-tuning for enterprise clients.

When to Fine-Tune

Fine-tuning is not always the answer. Consider it when:

• Prompt engineering plateaus: you have optimized prompts but accuracy is still below requirements.
• Consistent output format is critical: the model needs to produce structured data reliably.
• Domain vocabulary is specialized: medical, legal, financial, or technical terminology that generic models handle poorly.
• Cost optimization: a smaller fine-tuned model can replace a larger, more expensive one.

Data Preparation

The quality of your fine-tuning data determines the quality of your model. Our process:

1. Collect examples: gather 500-5,000 high-quality input-output pairs from your domain.
2. Clean ruthlessly: remove duplicates, fix formatting, ensure consistency.
3. Stratify: ensure your training set covers the full range of scenarios you expect in production.
4. Hold out a test set: reserve 15-20% of data for evaluation. Never train on your test set.

For instruction-following tasks, format your data as conversations with clear system prompts, user queries, and ideal assistant responses.

Choosing Your Approach

Approach	Training Data Needed	Compute Cost	When to Use
Prompt Engineering	0 examples	None	Start here always
Few-Shot Learning	5-20 examples	None	Simple classification
LoRA / QLoRA	500-2,000 examples	Low-Medium	Most enterprise use cases
Full Fine-Tuning	5,000+ examples	High	Maximum customization

We recommend LoRA (Low-Rank Adaptation) for most enterprise projects. It achieves 90-95% of full fine-tuning quality at a fraction of the compute cost and training time.

Training Pipeline

Our standard fine-tuning pipeline:

1. Base model selection: choose the smallest model that handles your task class well.
2. Hyperparameter search: learning rate, batch size, and number of epochs are the three most impactful parameters.
3. Training with validation: monitor loss on the validation set to detect overfitting early.
4. Checkpoint selection: pick the checkpoint with the best validation metric, not the last one.

Key lesson: more epochs is not always better. We typically see optimal results between 2-5 epochs for LoRA fine-tuning.

Evaluation

Automated metrics only tell part of the story:

• Task-specific metrics: accuracy, F1, BLEU, or ROUGE depending on the task.
• Human evaluation: have domain experts rate 100-200 outputs on a rubric.
• A/B testing: compare the fine-tuned model against the base model on real user queries.
• Regression testing: ensure the model has not lost capabilities on adjacent tasks.

Production Deployment

• Version your models: tag every fine-tuned model with training data version, hyperparameters, and evaluation scores.
• Gradual rollout: route 10% of traffic to the new model, monitor, then increase.
• Continuous monitoring: track output quality metrics in production. Model drift is real.
• Retraining schedule: plan quarterly retraining as your domain data evolves.

Cost Analysis

For a typical enterprise use case processing 10,000 queries per day:

• Base GPT-4 cost: approximately $1,500/month.
• Fine-tuned GPT-4o-mini: approximately $200/month with comparable quality.
• Fine-tuned open-source (Llama): approximately $50/month on self-hosted infrastructure.

Fine-tuning pays for itself within weeks for high-volume applications.

Conclusion

Fine-tuning is an investment in precision. When your use case demands consistent, domain-specific performance, a well-tuned model delivers better accuracy at lower cost than prompting a general-purpose model. The key is starting with clean data, choosing the right approach, and measuring rigorously.