Building RAG Context Prompts with ChromaDB and Python

As generative AI applications become more common, retrieving the right information to ground your language models is essential. Building a Retrieval-Augmented Generation (RAG) pipeline requires understanding how to query a vector database and structure the retrieved context into a prompt that an LLM can actually use. This text-based course guides you through the core concepts of vector embeddings and context construction. You will transition from understanding basic query mechanisms to programmatically assembling precise prompts that keep your AI applications accurate and context-aware. What you'll learn: • Understand the foundational concepts of vector databases, embeddings, and semantic search • Query ChromaDB programmatically to retrieve highly relevant document snippets • Design and structure context prompts that prevent hallucination in LLMs • Apply prompt engineering best practices to format retrieved text for model consumption • Clean and preprocess raw data to optimize vector search results • Implement a complete RAG workflow using Python scripts and modern database patterns. You will start by mastering the essential terminology of embeddings and vector databases before diving into practical, written exercises. Through clear code explanations, you will learn how to query storage, extract relevant data, and build robust prompt templates. This course is designed for beginner Python developers and aspiring AI engineers who want to understand the mechanics of RAG. No prior experience with vector databases or machine learning is required. Start reading today and build your first context-driven AI pipeline from scratch.