AI Glossary
Fine-tuning
Process of retraining an AI model with your company's specific data so it specializes in your domain or tone.
Fine-tuning adapts a pretrained model to a specific domain. It's like giving the model a master's degree in your industry.
When to fine-tune
- When you need a very specific tone or style that prompting can't achieve
- When the model needs specialized knowledge (medical, legal, technical jargon)
- When you need faster and cheaper responses (smaller fine-tuned models)
When NOT to fine-tune
- If RAG solves your problem (cheaper and more flexible)
- If you can achieve the result with prompt engineering
- If your data changes frequently (you'd have to retrain constantly)
Cost and complexity
Fine-tuning requires quality training data (minimum 50-100 examples), GPU infrastructure, and technical knowledge. For most SMBs, RAG + prompt engineering covers 90% of use cases.
Related services
Related terms
AI Agent
An AI system that can plan, execute actions, and use tools autonomously to complete complex tasks.
Prompt Engineering
The discipline of designing precise instructions for AI models that maximize the quality and relevance of responses.
LLM (Large Language Model)
An artificial intelligence model trained on large volumes of text that can understand and generate natural language with high quality.
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