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

Summary: The video discusses how to obtain better responses from large language models (LLMs) by employing three main methods: Retrieval Augmented Generation (RAG), fine-tuning, and prompt engineering. Each approach has its advantages and drawbacks, with RAG focusing on real-time data retrieval, fine-tuning enhancing specialization, and prompt engineering improving query specificity.

Keypoints:

The modern equivalent of Googling oneself is querying a chatbot for information.

Responses from LLMs can vary significantly based on their training data and knowledge cutoff dates.

Three methods to improve LLM responses are:

• Retrieval Augmented Generation (RAG): Involves searching for up-to-date data and incorporating it into responses.
• Fine-tuning: Specializes an existing model on a focused dataset to enhance its expertise.
• Prompt Engineering: Involves crafting specific queries to trigger more relevant and accurate responses.

RAG combines retrieval, augmentation, and generation to improve response quality but adds processing costs and latency.

Fine-tuning updates a model’s internal parameters through specialized training but can be complex and requires substantial computational resources.

Prompt engineering allows users to specify input formats and contexts to direct the model’s focus without changing its underlying architecture.

Combining these three methods can enhance the effectiveness of AI systems in various domains, such as legal applications.

Choosing the right method depends on specific needs, balancing flexibility, resource requirements, and the need for up-to-date information.