Best Practices & Expert Tips

Proven strategies and techniques to maximize your fine-tuning success

⭐ Core Principles

Quantity vs Quality

100 excellent examples beat 1000 mediocre ones. Aim for quality first, then quantity. Review and improve your examples regularly.

Diversity is Key

Include different question styles, formats, and difficulty levels. Don't make all examples sound the same—real users ask questions differently.

Use Real Conversations

Import from Claude, Cursor, or your actual user interactions. Real conversations are better than synthetic examples.

Cover Edge Cases

Include examples of common mistakes, error handling, and unusual scenarios. Your model should know what NOT to do too.

Be Consistent

Use the same system prompt for related examples. Consistent tone and style help the model learn your brand voice.

Show, Don't Just Tell

Good outputs include code examples, step-by-step explanations, and concrete demonstrations. Abstract explanations alone are weak.

Iterate & Improve

Don't aim for perfect on the first try. Create examples, review them, improve them. It's a process, not a one-time task.

Test Your Dataset

Before training 1000 examples, try training on 100 first. Test if the model learns the right patterns. Fix issues early.

🚀 Advanced Techniques

Chain of Thought Examples

For complex reasoning tasks, include examples where the AI shows its thinking process before giving the answer. This teaches the model to reason step-by-step.

Example: "Let me think through this... First, I'll check X. Then, I'll verify Y. Based on this analysis, the answer is Z."

Few-Shot Prompting in Examples

Include examples where the instruction provides 2-3 examples of the desired format, then asks for a new one in the same format.

Negative Examples (What NOT to do)

Occasionally include examples where the instruction asks for something and the output explains why it's not possible or inappropriate. This helps the model learn boundaries.

Progressive Complexity

Create sequences of examples that build on each other. Start with basics, then gradually introduce complexity. This helps the model learn dependencies between concepts.

⚠️

Common Mistakes to Avoid

❌ Mistake #1: Vague Instructions

Instructions like "help me with Python" or "explain this" are too vague. The model can't learn from ambiguous inputs.

Instead: "How do I handle file reading errors in Python with proper exception handling and user-friendly error messages?"

❌ Mistake #2: Inconsistent Style

Mixing very formal responses with casual ones, or changing the AI's "personality" between examples confuses the model.

Instead: Pick a consistent tone and stick with it. Use system prompts to maintain consistency across related examples.

❌ Mistake #3: Too Many Similar Examples

Creating 100 variations of "How do I print in Python?" doesn't help. It just bloats your dataset without adding value.

Instead: Cover diverse topics and question types. Quality diversity beats quantity similarity.

❌ Mistake #4: Ignoring Quality Ratings

Marking every example as 5-star or not using the quality system means you'll train on bad data.

Instead: Be honest with ratings. Reject 1-2 star examples. Only approve 3+ stars. Review and improve constantly.

❌ Mistake #5: Rushing to 1000

Focusing only on the number without caring about quality. Bad examples actively harm your model.

Instead: Focus on creating 10 excellent examples, then 50, then 100. Build quality incrementally.

❌ Mistake #6: Not Using Categories

Leaving all examples as "general" makes it impossible to track dataset balance or find specific types later.

Instead: Always categorize. Aim for a balanced mix: 40% coding, 30% explanation, 20% analysis, 10% other.

❌ Mistake #7: Outputs Too Short

One-sentence outputs don't teach the model enough. They learn to be brief instead of helpful.

Instead: Outputs should be 3-10 sentences minimum for simple questions, longer for complex ones. Include examples and explanations.

❌ Mistake #8: Wrong Difficulty Mix

Having only beginner OR only advanced examples limits your model. It won't handle the full range of user needs.

Instead: Aim for 30% beginner, 50% intermediate, 20% advanced. This prepares your model for all user levels.