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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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The Prompt Input Field

The prompt input field in GlowStudio: GlowStudio prompt input field
Remember to always include the variables {kb_context} and {about_context} in your prompt. Without these, the agent won’t have access to the retrieved chunks from the RAG (Retrieval-Augmented Generation) system.

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Key Concepts

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: Copy1. Understand the problem more thoroughly 2. Show its reasoning process 3. Arrive at more accurate conclusionsExample:
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. Copy- One-shot learning: Providing one example
  • Two-shot learning: Providing two examples
  • Few-shot learning: Providing a few (typically 3-5) examples
Example:

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Next Steps

Now that you’ve got an overview of prompt engineering, explore the following sections to deepen your understanding:

Formatting Your Prompts

Learn how to structure and format your prompts effectively using Markdown and XML.

Step-by-Step Prompt Guide

Follow our comprehensive guide to crafting the perfect prompt.

TechTalk Example

See a real-world example of a tech-focused prompt using Markdown.

BakeMate Example

Explore a culinary-themed prompt example using Markdown.