Fine-Tuning OpenAI Models for RAG with Qdrant: A Hands-On Guide

Explore a complete walkthrough on fine-tuning OpenAI models for Retrieval Augmented Generation (RAG), enhanced by Qdrant and few-shot learning to minimize hallucinations. Using gpt-3.5-turbo on SQuAD, this practical tutorial is ideal for ML practitioners and AI engineers mastering tailored workflows.

This guide offers a detailed, step-by-step example of fine-tuning OpenAI models tailored for Retrieval Augmented Generation (RAG).

We'll integrate Qdrant alongside few-shot learning to elevate the model's effectiveness and curb hallucinations. It acts as a hands-on resource for machine learning practitioners, data scientists, and AI engineers aiming to unlock OpenAI models' potential for targeted applications. 🚀

Note: This notebook employs the gpt-3.5-turbo model. Fine-tuning on the SQuAD dataset with this setup yields only minimal gains for more advanced models such as gpt-4o or gpt-4.1. As such, this notebook is primarily intended as a guide for fine-tuning workflows and retrieval-augmented generation (RAG) practices.

Why should you read this blog?

You want to learn how to

• Fine-tune OpenAI models for specific use-cases

• Use Qdrant to improve the performance of your RAG model

• Use fine-tuning to improve the correctness of your RAG model and reduce hallucinations

To begin, we've selected a dataset where we've a guarantee that the retrieval is perfect. We've selected a subset of the SQuAD dataset, which is a collection of questions and answers about Wikipedia articles. We've also included samples where the answer is not present in the context, to demonstrate how RAG handles this case.

Table of Contents

• Setting up the Environment

Section A: Zero-Shot Learning

• Data Preparation: SQuADv2 Dataset

• Answering using Base gpt-3.5-turbo-0613 model

• Fine-tuning and Answering using Fine-tuned model

• Evaluation: How well does the model perform?

Section B: Few-Shot Learning

Using Qdrant to Improve RAG Prompt

Fine-Tuning OpenAI Model with Qdrant

Evaluation

Conclusion

Aggregate Results

• Observations