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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.

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.

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:
What's the result of 25 * 18?
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:
Translate the following English phrases to French. Here are two examples:

English: Hello, how are you?
French: Bonjour, comment allez-vous ?

English: Where is the nearest restaurant?
French: Où est le restaurant le plus proche ?

Now, translate this:
English: I would like to book a hotel room.

Next Steps

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