Fine-tuning — when prompt engineering hits its ceiling.
Domain-specific model adaptation for specialized tasks where general-purpose models consistently underperform. Custom synthetic data pipeline, multiple training runs, rigorous evaluation framework.
What you get
Use case validation — honest assessment of whether fine-tuning will outperform prompt engineering for your specific task
Synthetic data pipeline — automated generation of high-quality training examples at scale
Training data curation — review, filtering, and quality control of training examples
Training runs with systematic hyperparameter exploration
Evaluation framework — benchmark against your specific task, compared to baseline (prompted general model)
Deployed fine-tuned model with API access
Documentation: training data structure, evaluation methodology, retraining guide
How it works
Feasibility assessment
Before anything else: determine whether fine-tuning will actually outperform a well-prompted general model for your task. Most tasks don't need fine-tuning. If yours doesn't, I'll tell you and recommend what does.
Data strategy
Define the data format, quality criteria, and volume target for training. Design the synthetic data generation pipeline. Agree on the evaluation benchmark before generating a single example.
Data pipeline + generation
Build and run the synthetic data pipeline. Human review of a sample for quality. Generate training corpus to target volume.
Training + iteration
Initial training run. Evaluate against benchmark. Iterate — adjust data, prompt format, or hyperparameters based on results. Typically 2–3 training rounds.
Evaluation + deployment
Final benchmark evaluation. Deploy fine-tuned model. Document performance improvements vs. baseline. Retraining guide delivered.
Tech stack
FAQs
When does fine-tuning actually make sense?
Fine-tuning makes sense when: (1) you need consistent formatting or style that's difficult to prompt reliably, (2) you have a specialized domain where general models consistently underperform, (3) you need significant latency improvement by running a smaller specialized model, or (4) you need to reduce context length by baking in domain knowledge. It does NOT make sense to add new knowledge — RAG is better for that.
How much training data do I need?
Typically 100–1000 high-quality examples for supervised fine-tuning on a specific task. More important than volume is quality and task specificity. I use synthetic data generation to get to the target volume without requiring you to manually label thousands of examples.
How do you measure whether fine-tuning actually worked?
Before training starts, we define a benchmark: a test set of examples with known correct outputs, evaluated on your specific quality criteria. After training, the fine-tuned model is benchmarked against the same test set. If it doesn't beat the baseline, we don't claim success.
Why does fine-tuning cost more than other services?
Data pipeline design and generation, multiple training runs (each costs compute), rigorous evaluation, and the higher-stakes nature of the output. A bad automation is annoying. A bad fine-tuned model corrupts every downstream task it's used for. The evaluation framework is non-optional.