Why Fine-Tune?
Foundation models like GPT-4, Claude, and Llama are impressive — but they're generalists. They know a little about everything and a lot about nothing specific to your business.
Fine-tuning teaches models your domain, your terminology, your edge cases. The result? AI that performs like it was built just for you. Because it was.
When Fine-Tuning Makes Sense
✅ You Should Fine-Tune When:
- Domain-specific language — Medical, legal, technical jargon that base models handle poorly
- Proprietary workflows — Internal processes that aren't in public training data
- Consistent formatting — Need structured outputs (JSON, specific report formats)
- Cost optimization — Smaller fine-tuned models can beat larger foundation models
- Latency requirements — Faster inference from smaller, specialized models
- Data privacy — Keep sensitive data out of third-party APIs
❌ You Don't Need Fine-Tuning When:
- Good prompting solves your problem (try prompt engineering first)
- You have <100 quality examples (not enough data)
- Your use case is general knowledge (foundation models excel here)
- You're just starting and need to iterate quickly (prompt engineering is faster)
Our Fine-Tuning Methodology
Phase 1: Data Assessment & Preparation (Weeks 1-2)
- Data audit — Evaluate your existing data quality and quantity
- Labeling strategy — Design efficient annotation workflows
- Synthetic data generation — Augment with AI-generated examples if needed
- Data cleaning — Remove noise, duplicates, and edge cases
- Train/eval split — Proper validation methodology
Phase 2: Baseline Evaluation (Week 2)
- Test base model performance on your task
- Establish clear metrics (accuracy, F1, BLEU, human eval, etc.)
- Document current failure modes
- Set improvement targets (what's good enough?)
Phase 3: Fine-Tuning (Weeks 3-4)
- Model selection — Choose right base model (GPT, Llama, Mistral, etc.)
- Hyperparameter tuning — Learning rate, batch size, epochs
- Training runs — Multiple experiments to find optimal config
- Evaluation — Track metrics during training, prevent overfitting
- Ablation studies — Understand what's driving improvements
Phase 4: RLHF (Optional, Weeks 5-6)
- Human feedback collection — Gather preferences on model outputs
- Reward model training — Teach model what "good" looks like
- Policy optimization — PPO or DPO for alignment
- Safety guardrails — Ensure model behaves responsibly
Phase 5: Deployment & Monitoring (Week 6+)
- A/B testing — Compare fine-tuned vs base model in production
- Gradual rollout — Start with small traffic percentage
- Monitoring setup — Track performance, latency, costs
- Retraining strategy — Plan for model drift and updates
Fine-Tuning Techniques We Use
Supervised Fine-Tuning (SFT)
Train on labeled examples of desired behavior:
- Full fine-tuning — Update all model weights
- LoRA (Low-Rank Adaptation) — Parameter-efficient, faster training
- Prompt tuning — Learn soft prompts without changing model
- Adapter layers — Add small trainable modules
Reinforcement Learning from Human Feedback (RLHF)
Align model outputs with human preferences:
- Reward modeling — Learn what humans prefer
- PPO (Proximal Policy Optimization) — Standard RL approach
- DPO (Direct Preference Optimization) — Simpler alternative to PPO
- Constitutional AI — Self-critique and improvement
Few-Shot Fine-Tuning
When you have limited data:
- Meta-learning — Learn to learn from few examples
- Transfer learning — Leverage similar tasks
- Data augmentation — Synthetic examples to boost training set
Real-World Results
Case Study: Legal Document Analysis
Client: Law firm needing contract review automation
Challenge: GPT-4 missed legal nuances, 60% accuracy on key clause detection
Solution: Fine-tuned Llama 3 70B on 10,000 annotated contracts
Results:
- Accuracy increased to 94%
- 3x faster review time
- 80% cost reduction (self-hosted vs API)
- ROI achieved in 4 months
Case Study: Customer Support Automation
Client: SaaS company with 50K+ support tickets/month
Challenge: Generic chatbots gave unhelpful responses, 30% resolution rate
Solution: Fine-tuned GPT-3.5 Turbo on 50,000 historical support interactions
Results:
- Resolution rate increased to 75%
- Customer satisfaction score +40%
- Support team freed to handle complex cases
- $200K annual savings in support costs
Case Study: Code Generation for Specific Framework
Client: Enterprise with custom internal framework
Challenge: GPT-4 couldn't generate correct code for their proprietary system
Solution: Fine-tuned Code Llama on internal codebase (5M tokens)
Results:
- 90% reduction in syntax errors
- Developer productivity +50%
- Faster onboarding for new engineers
Models We Fine-Tune
Open-Source Models
- Llama 3 (8B, 70B, 405B) — Meta's powerful open models
- Mistral (7B, Mixtral 8x7B) — Fast, efficient, European
- Code Llama — Specialized for code generation
- Falcon — Strong performance, permissive license
Proprietary Models
- GPT-3.5/4 — OpenAI's API fine-tuning
- Claude — Anthropic's models (when fine-tuning available)
- Gemini — Google's models for specialized tasks
Domain-Specific Models
- BioBERT, PubMedBERT — Medical/scientific text
- FinBERT — Financial sentiment analysis
- LegalBERT — Legal document understanding
ROI Calculator
How much could fine-tuning save you?
Typical Cost Comparison (per 1M tokens):
- GPT-4 API: $60
- Fine-tuned GPT-3.5: $12 (5x cheaper)
- Self-hosted fine-tuned Llama 3 70B: $4 (15x cheaper)
Latency Comparison:
- GPT-4 API: 2-5 seconds
- Self-hosted fine-tuned model: 200-500ms (10x faster)
For high-volume use cases, fine-tuning pays for itself in weeks.
What We Deliver
- Fine-tuned model weights — Yours to own and deploy
- Training pipeline — Reproducible, version-controlled
- Evaluation framework — Benchmarks and test suites
- Deployment guide — How to serve the model in production
- Retraining playbook — Process for future updates
- Performance report — Before/after metrics with analysis
Pricing
- Fixed-price fine-tuning project — Clear scope and deliverables
- Ongoing optimization retainer — Continuous improvement and retraining
- Consulting & feasibility study — Should you fine-tune? We'll tell you honestly.
Ready to Optimize Your AI?
Book a consultation. We'll evaluate whether fine-tuning makes sense for your use case and estimate the ROI.
No overselling — if prompting or a base model works, we'll tell you. If fine-tuning will unlock value, we'll show you how.