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Prompt engineering is the art and science of crafting effective instructions for AI models, particularly Large Language Models (LLMs). A well-designed prompt can significantly enhance the quality, relevance, and safety of AI-generated responses. This guide will walk you through the key concepts and best practices in prompt engineering.

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Screenshot of the prompt input field in glowstudio:
Rememeber to always include the variables {kb_context} and {about_context} in your prompt or else the agent wont know the retrieved chunks from the RAG.

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Key Concepts

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Chain of Thought (CoT) reasoning is a technique that involves breaking down complex problems into a series of intermediate steps. This approach helps the AI model to:
  1. Understand the problem more thoroughly
  2. Show its reasoning process
  3. Arrive at more accurate conclusions
Example:
Few-shot learning is a technique where you provide the AI with a small number of examples to guide its understanding of the task. This can be particularly useful when you want the AI to follow a specific format or style in its responses.
  • One-shot learning: Providing one example
  • Two-shot learning: Providing two examples
  • Few-shot learning: Providing a few (typically 3-5) examples
Example: