Overview
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.
{kb_context} and {about_context} in your prompt or else the agent wont know the retrieved chunks from the RAG.Key Concepts
Chain of Thought (CoT) Reasoning
Chain of Thought (CoT) Reasoning
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:
-
Understand the problem more thoroughly
-
Show its reasoning process
-
Arrive at more accurate conclusions
Example:
Few-Shot Learning
Few-Shot Learning
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: