Introduction to Fine-tuning with Promptrepo

A general AI model gets close on your task but not quite right: it doesn't know your categories or your format, and longer prompts only go so far. Training a model from scratch is slow and needs a lot of data. Fine-tuning sits in between: you take a trained model and teach it with your own examples. With Promptrepo, you do that from Google Sheets without code, and the model answers in your categories and your format.

Let's start. Create your AI model from one example and try it as you read.

Fine-tuning is the process of taking a pre-trained AI model and optimizing it for a specific task by training it further on a specialized dataset. Instead of training a model from scratch, fine-tuning builds on what a model already knows and teaches it your task.

Why Fine-tune AI Models?

  1. Higher Accuracy: Pre-trained models are general-purpose. Fine-tuning makes them domain-specific, improving performance.
  2. Less Data Required: Since the model already has foundational knowledge, we only need a smaller, high-quality dataset.
  3. Better Efficiency: Fine-tuning can be significantly faster and cheaper than training a model from scratch.

Fine-tuning Workflow in Promptrepo

In Promptrepo, fine-tuning has four steps:
  1. Define Your Task: Classification, Extraction or Generation
  2. Prepare Training Data: Curate examples that demonstrate the desired behavior
  3. Train the Model: Use Promptrepo to fine-tune on your dataset
  4. Evaluate & Iterate: Test and improve the model based on real-world performance

Example: Fine-tuning a Model for Food Classification

Let's say we want to classify food items into different categories such as Red Meat, Beverages, Cheese or General Foods. Instead of building a classifier from scratch, we fine-tune an existing model with food-specific data.
Steps:
  1. Collect Data: Create a spreadsheet with food names & their respective categories.
  2. Fine-tune in Promptrepo: Enter one example, publish the model and add your spreadsheet to its Google Sheet.
  3. Test & Deploy: The model can now classify new food items accurately.
Fine-tuning builds an accurate model from a small set of your own examples. The next articles show how to build classification, extraction and generation models with Promptrepo.

Related links

Build a classification model
Build an extraction model
Build a generative model

Summary

If a general AI model is not accurate enough and training one from scratch is too much work, fine-tuning is the way in between. With Promptrepo, you create your AI model from your own examples.

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