Title

AWS re:Invent 2023 - Prompt engineering best practices for LLMs on Amazon Bedrock (AIM377)

Summary

• John Baker, a principal engineer with AWS Bedrock, and Nicholas Marwell from Anthropic discuss prompt engineering for large language models (LLMs).
• They cover the importance of generating predictable, consistent results from LLMs for customer-facing applications.
• The session includes general techniques in prompt engineering, system design around Retrieval Augmented Generation (RAG), and optimization for Anthropic's models.
• Examples demonstrate how prompt engineering can change the tone and complexity of responses from LLMs.
• Techniques such as one-shot prompting, few-shot prompting, chain of thought (COT), and RAG are explained.
• Nicholas provides insights into prompt engineering philosophy, the empirical approach to prompt development, and specific strategies for Anthropic's Claude model.
• Advanced techniques like prompt chaining and tool use are introduced, with a focus on how they can enhance functionality and flexibility in applications.
• The session concludes with an invitation for further discussion and a call to action to explore more content.

Insights

• Prompt Engineering: It's a method to guide LLMs to produce desired outputs by providing context and examples. It's akin to giving detailed instructions to a human.
• Personas and Tone: Setting a persona for the LLM can significantly influence the tone and complexity of its responses, which is crucial for customer interaction.
• Chain of Thought: Encouraging LLMs to break down complex problems into steps can improve the accuracy of the answers and provide transparency into the model's reasoning.
• Retrieval Augmented Generation (RAG): This technique involves using an external knowledge store to retrieve information that augments the LLM's responses, making them more relevant and accurate.
• Anthropic's Claude Model: Specific to Claude, the use of special tokens, XML tags, and role prompting can greatly enhance the model's performance. Claude is also trained to work well with structured data and can benefit from detailed tool descriptions and function calls.
• Empirical Approach: Developing effective prompts is an empirical process that involves creating diverse test cases, iterating on prompts, and refining based on performance.
• Advanced Techniques: Prompt chaining and tool use can add complexity to LLMs' capabilities, allowing for more nuanced interactions and the ability to call external functions for additional data or actions.
• Documentation and SDKs: Good documentation is essential for prompt engineering, and SDKs like the one from Anthropic can simplify the process of integrating advanced prompting techniques with models like Claude.