richardleighdavies/prompt-engineering-in-practice
Practical code examples and implementations from the book "Prompt Engineering in Practice". Demonstrates text generation, prompt chaining, and prompt routing using Python and LangChain. Features real-world examples of interacting with OpenAI's GPT models, structured output handling, and multi-step prompt workflows.
These practical code examples help you create better interactions with AI models like ChatGPT. They demonstrate how to craft, refine, and organize your prompts to get more accurate and useful text, even for complex multi-step conversations. This is for AI practitioners, content creators, marketers, or anyone who regularly uses large language models and wants to improve their output.
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Use this if you need to design, test, and optimize prompts to get reliable, high-quality text or structured data from AI models.
Not ideal if you're looking for a low-code solution or a tool that doesn't require direct interaction with Python code and API keys.
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MIT
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Last pushed
Aug 04, 2025
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