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In this specialization, you’ll learn advanced techniques for building and deploying Retrieval-Augmented Generation (RAG) systems. You’ll explore methods like query expansion, re-ranking, and dense passage retrieval, while gaining hands-on experience with RAG's core components. The specialization also helps you navigate RAG deployment challenges and provides solutions for real-world applications.
The specialization is divided into four parts. It begins with an introduction to RAG concepts, followed by developing RAG applications using LlamaIndex and integrating data with LLMs. Next, you'll explore using Knowledge Graphs to enhance AI systems, and finally, build multimodal systems by combining RAG with GPT for smarter solutions.
This specialization is perfect for intermediate learners with a basic understanding of AI and programming. It’s suitable for those interested in AI, software development, and data science. Familiarity with Python is required.
By the end of the specialization, you’ll be able to develop RAG applications, use Knowledge Graphs to improve AI systems, and create multimodal systems combining RAG with GPT.
Praktisches Lernprojekt
The specialization includes hands-on projects where learners will apply advanced RAG techniques, create AI applications using LlamaIndex, and design Knowledge Graphs. These projects focus on real-world challenges like enhancing search capabilities, integrating diverse data sources, and building multimodal systems using GPT. Learners will gain practical experience in developing smarter AI-driven systems and solving complex problems with RAG and multimodal technologies.
