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Prompt Engineering, RAG, and Fine-tuning: Benefits and When to Use
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Explore the difference between Prompt Engineering, Retrieval-augmented Generation (RAG), and Fine-tuning in this detailed overview.
01:14 Prompt Engineering + RAG
02:50 How Retrieval Augmented Generation Works - Step-by-step
06:23 What is fine-tuning?
08:25 Fine-tuning misconceptions debunked
09:53 Fine-tuning strategies
13:25 Putting it all together
13:44 Final review and comparison of techniques
Prompt engineering is a powerful tool to steer a large language model's behavior by providing instructions and examples directly in the prompt. RAG, a type of knowledge retrieval, grounds the model's responses to reality by pulling in dynamic and trusted external data sources. Fine tuning trains the model on your own examples to narrow and customize its outputs.
Each approach has its strengths. Prompt engineering is fast and intuitive, while RAG connects real-time data. Fine-tuning bakes in your style, tone, and formatting. The good news is they can all work together! Use prompt engineering to rapidly prototype, RAG to leverage your knowledge base, and fine-tuning to improve speed, cost, and quality.
In this video, Mark Hennings explains the unique value of each technique and shows how you can combine them for the best of all worlds. See examples of few-shot learning prompts and how they can be upgraded with fine-tuning datasets. Learn the specific fine-tuning strategies for optimizing cost, speed and quality.
There's something for everyone here. Whether you have experience with machine learning or just starting out with AI, this video will expand your understanding and give you ideas for improving your generative AI projects.
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