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Learn Retrieval Augmented Generation
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Learn all about modern search engine techniques like keyword, vector, semantic and LLM-enhanced search. In this course you'll implement different search techniques from scratch in Python everything from simple keyword search up to a fully functional Retrieval Augmented Generation (RAG) pipeline using the Gemini API.
Instructor
Isaac Flath
Isaac Flath was the Head of Data Science consulting at Centriam beforing making the switch to speaking and education in 2025. He's a data and AI specialist that has been using Python to build custom AI powered solutions for companies that want to understand their own information better.
Course details
40 hours
video
Included
Subscription
What you'll learn
Build a fully functional Retrieval Augmented Generation (RAG) pipeline in Python
Implement advanced search techniques including keyword, vector, and semantic search from scratch
Master the use of large language models (LLMs) to enhance query understanding and retrieval workflows
Develop hybrid search systems that combine lexical and semantic scoring for improved document ranking
Prerequisites
Strong Python programming knowledge
Understanding of programming fundamentals
Familiarity with APIs and web development
Who this course is for
Python developers interested in RAG and search technologies
Data scientists working with LLMs and AI applications
Backend developers building AI-powered search systems
Engineers wanting to master modern retrieval techniques
Curriculum
Preprocessing
TF-IDF
Keyword Search
Semantic Search
Chunking
Hybrid Search
LLMs
Reranking
Evaluation
Augmented Generation
Agentic
Multimodal
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