From the course: Build with AI: LLM-Powered Applications with Streamlit

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Construct effective RAG prompts for better LLM answers

Construct effective RAG prompts for better LLM answers - Python Tutorial

From the course: Build with AI: LLM-Powered Applications with Streamlit

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Construct effective RAG prompts for better LLM answers

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- [Instructor] This video will purely be front end. In a real retrieval augmented generation pipeline, your app will automatically fetch relevant chunks of context, using Vice. But before you automate that, it's helpful to manually construct the RAG prompts so you understand the formatting structure. This is a valuable skill since it shows how context and questions combine to guide the AI's response. In the next lesson, you'll automate this, using embeddings and vector search. For now though, let's manually simulate the middle part of that flow. Let's work with the file 03_07b.py, and you'll import your streamlet package and write a title such as Construct RAG Prompts. You'll then want to provide a text area for pacing context snippets. So again, this is going to be your user pasting these snippets in. So you'll have context_snippets = st.text_area. You'll have parentheses, and then you can put a prompt in here, such as Paste retrieved context snippets, and then make sure you note…

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