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

  1. Definition of fine-tuning
  2. Importance and use cases
  3. Step-by-step fine-tuning workflow
  4. Cost estimation strategies
  5. Executing the fine-tuning process
  6. 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 openai Python package installed (pip install openai)
TopicDescriptionExample Command
Data PreparationFormatting training data in JSONL/CSVN/A
Fine-Tuning Job LaunchStarting the fine-tuning process via API or CLIopenai api fine_tunes.create …
Monitoring & EvaluationTracking job status and assessing model accuracyopenai api fine_tunes.get -i <JOB_ID>
DeploymentLoading your custom model for inferenceopenai.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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