Get-Things-Done-with-Prompt-Engineering-and-LangChain and prompt-engineering-in-practice
About Get-Things-Done-with-Prompt-Engineering-and-LangChain
curiousily/Get-Things-Done-with-Prompt-Engineering-and-LangChain
LangChain & Prompt Engineering tutorials on Large Language Models (LLMs) such as ChatGPT with custom data. Jupyter notebooks on loading and indexing data, creating prompt templates, CSV agents, and using retrieval QA chains to query the custom data. Projects for using a private LLM (Llama 2) for chat with PDF files, tweets sentiment analysis.
This project helps AI application developers build custom applications with large language models like ChatGPT. It guides you through integrating your own data sources, creating intelligent agents, and building chatbots that can understand and respond to specific queries. The output is a functional AI application, such as a sentiment analyzer for social media or a chatbot that can answer questions based on your documents. AI/ML engineers and data scientists looking to leverage LLMs for bespoke solutions are the target users.
About prompt-engineering-in-practice
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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