Mastering Generative AI with OpenAI
Fine tuning GPT 3 with a Custom Dataset
Section Intro
Introduction to Fine-Tuning Custom Datasets
Fine-tuning enables you to adapt large language models (LLMs) to specialized domains by training them on your own data. While dynamic context injection customizes outputs at inference time, fine-tuning updates the model’s parameters so it “remembers” your unique content permanently. This approach ensures more accurate, context-aware responses for queries outside the model’s original training set.
What You’ll Learn in This Section
- Definition of fine-tuning
- Importance and use cases
- Step-by-step fine-tuning workflow
- Cost estimation strategies
- Executing the fine-tuning process
- Deploying and calling your fine-tuned model
We’ll start by exploring why fine-tuning is critical, then move quickly into a hands-on demo. Let’s dive in!
Prerequisites
Make sure you have:
- An OpenAI API key
- A structured dataset in JSONL or CSV format
- The
openaiPython package installed (pip install openai)
| Topic | Description | Example Command |
|---|---|---|
| Data Preparation | Formatting training data in JSONL/CSV | N/A |
| Fine-Tuning Job Launch | Starting the fine-tuning process via API or CLI | openai api fine_tunes.create … |
| Monitoring & Evaluation | Tracking job status and assessing model accuracy | openai api fine_tunes.get -i <JOB_ID> |
| Deployment | Loading your custom model for inference | openai.ChatCompletion.create(model="ft-…", …) |
Why Fine-Tuning Matters
Fine-tuning yields models that:
- Understand niche terminology (e.g., legal, medical)
- Maintain consistent tone and formatting
- Improve accuracy on domain-specific tasks
Unlike prompt engineering alone, a fine-tuned model will not “forget” your custom logic or examples.
Proceed to the next sections for a detailed walkthrough of each step, from data formatting to deploying your specialized model.
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