RAG vs Fine-Tuning vs Prompt Engineering: Optimizing AI Models

About this Video

• Video Title: RAG vs Fine-Tuning vs Prompt Engineering: Optimizing AI Models
• Channel: IBM Technology
• Speakers: (Not explicitly named in transcript)
• Duration: 00:13:10

Introduction

This video explores three methods for improving the responses of large language models (LLMs): Retrieval Augmented Generation (RAG), fine-tuning, and prompt engineering. The video compares their strengths, weaknesses, and use cases, demonstrating how each approach can enhance an LLM's capabilities.

Key Takeaways

• RAG (Retrieval Augmented Generation): This method enhances LLM responses by retrieving relevant external data, converting it and the query into vector embeddings, and incorporating the retrieved information into the prompt before generating a response. It's valuable for up-to-date information and domain-specific knowledge but adds latency and infrastructure costs.
• Fine-tuning: This approach involves training a pre-trained LLM on a specialized dataset to improve its expertise in a specific domain. It's faster at inference time and offers deep domain expertise but requires substantial resources (GPUs, training data), and carries the risk of catastrophic forgetting.
• Prompt Engineering: This technique focuses on crafting effective prompts to guide the LLM's attention to relevant patterns learned during training. It offers flexibility and immediate results but is limited by the model's existing knowledge and requires trial and error.
• Combined Approaches: The video suggests that these techniques are often used in combination to leverage the strengths of each. For example, a legal AI system might use RAG for retrieving relevant cases, prompt engineering for formatting, and fine-tuning for mastering firm-specific policies.