Fine-Tuning
Fine-tuning is the process of further training a pre-trained AI model on specific data to customize its behavior for particular tasks or domains. While base models have general knowledge, fine-tuned models can follow specific coding conventions, understand proprietary systems, or match a particular style.
Example
A company fine-tunes an LLM on their internal codebase and documentation, so the model understands their specific APIs, naming conventions, and architectural patterns when generating code.
Fine-tuning customizes AI models beyond what prompting alone can achieve. While most vibe coders use off-the-shelf models, understanding fine-tuning helps you evaluate when custom models might be valuable.
How Fine-Tuning Works
- Start with a base model — Pre-trained on general data
- Prepare custom data — Examples of desired behavior
- Continue training — Model learns from your specific examples
- Result — Model that combines general knowledge with specific patterns
When Fine-Tuning Makes Sense
- Proprietary knowledge — Internal APIs, systems, terminology
- Specific style — Matching exact coding conventions
- Specialized domains — Industry-specific patterns
- High-volume tasks — When prompting overhead adds up
When It's Overkill
For most vibe coding, you don't need fine-tuning:
- Cursor Rules handle project conventions
- Few-shot prompting teaches specific patterns
- Context provides relevant code examples
- General models already know common frameworks
The Cost
Fine-tuning requires:
- Training data (hundreds to thousands of examples)
- Compute resources
- Ongoing maintenance as your codebase evolves
Most developers get excellent results with well-crafted prompts and good context management.
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Related Terms:
Emergent — An agentic vibe-coding platform that builds production-ready apps