Prompt Engineering - Basic Concepts For Developers
AI Summary
In this video, the presenter discusses foundational concepts necessary for creating applications on top of large language models (LLMs). Instead of a quick guide to becoming a prompt engineer, the focus is on understanding how LLMs work, specifically in programmatic contexts. The case study revolves around a web application that provides book recommendations without user interaction after the initial prompt.
Key topics covered include:
- The difference between LLM providers and models.
- The importance of context in prompts, especially for obtaining meaningful responses.
- Challenges related to urgency and latency in fetching additional user context.
- Strategies for structuring prompts effectively, including the introduction, context, and refocusing the question.
- The concept of chat interfaces and system roles in LLM interactions.
- The impact of token limits and how to manage them programmatically.
- Considerations for choosing the right model based on factors like cost, speed, and capability.
- The video emphasizes that understanding these concepts takes time and practical application, presenting a thoughtful approach to working with LLMs.