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Practice 04
AI Evaluation, Fine-Tuning & LLM Customization
Rigorous evaluation harnesses and fine-tuned models for product teams that need their LLM features to actually work.
Most LLM features ship on vibes. The teams that ship reliable AI products invest in evaluation infrastructure: a held-out set, a metric that correlates with user value, and a review loop that catches regressions before users do. We build that infrastructure, then use it to fine-tune, prompt, or distill models that meet the bar.
Outcomes
What you walk away with
- Custom evaluation harness with task-appropriate metrics and human review loops
- Synthetic and real-data fine-tuning datasets with documented quality controls
- LoRA / full fine-tunes with reproducible training and reporting
- Hallucination, bias, and class-imbalance diagnostics drawn from peer-reviewed methods
Engagement shapes
How we work together
- Eval harness build for a single product surface (4–6 weeks)
- Fine-tuning engagement: dataset, training, evaluation, and handoff
- Ongoing model-evaluation retainer for product teams
Audience
Who this is for
Mid-market tech companies integrating LLMs into products
Document-automation companies in legal, compliance, and HR
Enterprise software vendors adding AI features
AI-first startups that need ML rigor, not just demos
Next step
Ready to scope a evaluation & fine-tuning engagement?
A 30-minute call gets us to a yes, no, or a clear next step.
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