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.
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.
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:
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Human: What's the result of 25 * 18? AI: Let's approach this step-by-step: 1. First, let's break down 25 * 18 into (20 + 5) * 18 2. Now we can calculate: a) 20 * 18 = 360 b) 5 * 18 = 90 3. Adding these results: 360 + 90 = 450 Therefore, 25 * 18 = 450
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:
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Human: 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. AI: Based on the examples provided, here's the translation: English: I would like to book a hotel room. French: Je voudrais réserver une chambre d'hôtel.
