Understand RAG architecture - You will learn how to design data flow in a RAG system and clearly distinguish it from a standard LLM setup, so you can choose the right components for your own use cases.
Prepare data for retrieval - You will learn how to extract content from documents, split it into meaningful chunks, and tune chunking parameters so your downstream retrieval works more accurately.
Choose the right embeddings - You will compare embedding models and learn how to select vector representations based on data type, language, and business goal instead of relying on trial and error.
Use Qdrant confidently - You will configure the environment, create collections, and practice record operations in Qdrant, giving you a solid backend for applications built on vector retrieval.
Improve retrieval relevance - You will combine semantic search, metadata filtering, and reranking to return more relevant documents and reduce the number of weak or misleading answers from the system.
Build stronger prompts - You will practice creating prompt templates and combining user queries with retrieved context, so the model produces source-grounded answers instead of guesses or hallucinations.
Evaluate RAG quality - You will work with metrics and tools, including deepeval, that help you test answer quality, measure system performance, and automate validation as your RAG solution evolves.
Leave with a working blueprint - You will see a complete application for feeding a knowledge base and generating answers, making it much easier to transfer the techniques into a pilot or production project.
Python basics - You should be comfortable reading and writing simple Python code, using functions, imports, and text processing, because the training focuses on building working solution components.
API and library usage - You should have experience installing libraries, working in a development environment, and using APIs, so you can run the examples smoothly and integrate the components.
LLM fundamentals - You should understand what language models, prompts, and response context are, so you can move quickly into RAG, embeddings, and grounded answer generation.
Data and search basics - You should know the basics of working with text data, documents, and simple information retrieval, since the training covers parsing, chunking, and retrieval workflows.
