Prompt Engineering, RAG, Fine-tuning

In the video titled ‘Prompt Engineering, RAG, and Fine-tuning: Benefits and When to Use,’ Mark Hennings from Entry Point AI delves into the intricacies of three pivotal AI techniques. Prompt engineering is a method that guides a large language model’s behavior by embedding instructions and examples directly in the prompt. This technique is intuitive and allows for rapid prototyping. Retrieval-augmented generation (RAG) enhances this by integrating dynamic and trusted external data sources into the model’s responses, ensuring that the replies are grounded in real-time information. Fine-tuning, on the other hand, involves training the model on specific examples to narrow its output, reflecting a particular style, tone, and formatting. This method is particularly useful for instilling intuition that cannot be easily described through rules. Hennings emphasizes that each technique has its unique strengths: prompt engineering is fast and intuitive, RAG ensures responses are grounded in reality, and fine-tuning customizes the model’s behavior. Importantly, these techniques can be combined for optimal results. For instance, prompt engineering can be used for rapid prototyping, RAG for leveraging a knowledge base, and fine-tuning for improving speed, cost, and quality. Hennings also dispels common misconceptions about fine-tuning, such as the need for large datasets and high costs, highlighting modern parameter-efficient techniques. The video concludes with a discussion on how these techniques can work together, forming a comprehensive toolkit for enhancing large language models.