See how companies are training custom AI models to solve specific business problems. From customer support to code generation, discover what's possible with fine-tuning.
Real examples from companies using FineTune Lab to train production AI models
Train AI that answers customer questions in your brand voice
Real Example
SaaS company with 50,000 monthly tickets. Fine-tuned Llama 3.3 on 2 years of historical tickets + product docs. Result: 80% ticket deflection rate, 3-second average response time, 4.8/5 customer satisfaction.
Train AI that understands your codebase conventions
Real Example
Engineering team at fintech company. Fine-tuned on 3 years of internal React/TypeScript repos. Result: Generates boilerplate components, hooks, and tests matching team conventions. 40% faster feature development, consistent code quality.
Extract structured data from unstructured documents
Real Example
Legal tech startup processing 1,000+ contracts daily. Fine-tuned on 50,000 annotated contracts. Result: Extracts key terms, dates, parties, obligations with 98% accuracy. Reduced manual review time from 30 minutes to 2 minutes per contract.
Generate marketing copy, product descriptions, social media posts in your brand voice.
Classify text, detect sentiment, analyze customer feedback with domain-specific understanding.
Automate lead scoring, qualification, and initial outreach with personalized messaging.
Product recommendations, search, and conversational shopping assistants trained on your catalog.
Train models with specialized knowledge for regulated industries
Note: Domain-specific models require high-quality training data and rigorous evaluation. FineTune Lab's LLM-as-a-Judge and batch testing features help validate accuracy before production deployment.
Turn your use case into a production model in 4 simple steps
Gather historical examples: support tickets, code samples, documents, or conversations.
Convert to JSONL format with input/output pairs. Upload to FineTune Lab.
Select base model, configure training. Watch real-time metrics as model learns.
Evaluate with batch tests, deploy to production, monitor quality metrics.
Common questions about use cases and training data
For most use cases, 100-1,000 high-quality examples is a good starting point. Customer support and classification tasks can work with 100-500 examples. Code generation and document analysis benefit from 1,000-10,000 examples. Quality matters more than quantity - clean, representative data beats large noisy datasets.
Yes, you can train a single model on mixed data (e.g., support tickets + product questions + documentation). However, for best results, consider training separate models for distinct use cases and using a router to send queries to the right model. This maintains specialized performance.
For most use cases, start with Llama 3.3 or Mistral - they offer the best balance of quality and speed. For code generation, consider models pre-trained on code. For domain-specific tasks (legal, medical), look for specialized base models if available. Run batch tests to compare performance before committing.
Use FineTune Lab's batch testing to run your model on held-out test data. Enable LLM-as-a-Judge for automated scoring on accuracy, helpfulness, and safety. Test with real edge cases and failure modes. Compare predictions from different checkpoints side-by-side. Monitor Model Observability metrics in production.
Yes. As you collect new examples (customer conversations, support tickets, etc.), combine them with your original training data and retrain. FineTune Lab's Training Analytics lets you compare new versions against previous ones to ensure improvement. Versioning helps track which training data produced the best results.
These are common patterns, but fine-tuning works for many more use cases. The key is having input/output pairs showing the behavior you want. If you can demonstrate it with examples, you can fine-tune for it. Contact our team to discuss your specific use case and get guidance on training data requirements.
Start with our free tier. Train your first model in under 2 minutes.