DeepLearning.AI's Building and Evaluating Advanced RAG Applications teaches Retrieval-Augmented Generation (RAG), the technique that lets language models answer questions about your own documents. You learn to index documents, retrieve relevant chunks, and build evaluation pipelines to measure quality. The course is free on deeplearning.ai. After finishing, you can build Q&A systems over private data and evaluate their accuracy. The main limitation: RAG quality depends heavily on chunking strategy and embedding choice. This course covers the fundamentals but real production RAG requires more optimization.
RAG is the most common pattern for enterprise AI adoption, used by over 60% of organizations deploying LLMs (source: AI industry surveys 2024).
About This Course
A focused course on Retrieval-Augmented Generation (RAG). Covers advanced chunking, sentence-window retrieval, auto-merging retrieval, and evaluation with TruLens. Essential for any AI engineer. Free.
Course Details
Frequently Asked Questions
Is this course free?
Yes, free on deeplearning.ai.
What is RAG?
Retrieval-Augmented Generation: fetch relevant documents, then pass them to the LLM with the question. It's how you give AI access to your private knowledge base.
What tools does it use?
LlamaIndex and Qdrant (a vector database), with OpenAI for the language model.
After this, what's next?
Build a RAG system on your own data. Experiment with chunking, embedding models, and retrieval strategies.
How does RAG compare to fine-tuning?
RAG is cheaper, faster to update, and works for retrieval tasks. Fine-tuning changes the model's behavior and is harder to update.
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