O que é fine tuning

What is Fine Tuning?

Introduction to Fine Tuning

• Fine Tuning is a process that customizes AI models, like ChatGPT, to respond in a specific tone or style aligned with a company's needs.

• It involves training an already established model (e.g., GPT) using specific data relevant to the business, enhancing its ability to understand and use industry-specific vocabulary.

Purpose and Benefits of Fine Tuning

• The original model has broad knowledge but lacks familiarity with particular company language and customer interaction styles. Fine Tuning helps bridge this gap.

• Ideal for scenarios requiring consistent communication style across numerous interactions, such as formal responses or technical jargon in specialized fields.

When to Use Fine Tuning

• Effective when needing concise answers or specific formats; generic models may provide verbose responses that do not meet business requirements.

• Particularly useful in specialized domains (legal, medical), where the generic model may lack necessary expertise.

Limitations of Fine Tuning

• Not suitable for teaching factual knowledge (e.g., product prices); simpler methods like prompt engineering are recommended instead.

• Often misapplied; many users jump straight into Fine Tuning without first attempting effective prompt strategies which can be quicker and more cost-effective.

Implementation of Fine Tuning

• Requires high-quality data; hundreds of well-formatted examples are essential for successful outcomes.

• Involves preparing input-output example files for the model's training on platforms like OpenAI, resulting in a customized version of the AI tailored to user specifications.