Generative AIBeginner
Learn Generative AI in 23 Hours
freeCodeCamp's generative AI course covering prompt engineering, RAG, model deployment, and AI agents. Taught by Andrew Brown. Completely free on YouTube.
Free course on DeepLearning.AI
Building and Evaluating Advanced RAG Applications
2 hours 4.7(52,000 reviews)Free
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.
DeepLearning.AI's short courses, including this one, have been taken by over 6 million learners across its platform and Coursera (source: deeplearning.ai/about/).
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
- Platform
- DeepLearning.AI
- Language
- Generative AI
- Level
- intermediate
- Duration
- 2h
- Certificate
- No
- Cost
- Free
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.
More Generative AI Courses
Harvard CS50Certificate
Generative AIIntermediate
CS50's Introduction to Artificial Intelligence with Python
Harvard's introduction to AI with Python. Covers search, knowledge representation, uncertainty, optimisation, machine learning, neural networks, and NLP.
Generative AIIntermediate
Hugging Face LLM Course
Learn to build and deploy with transformers and large language models in this 12-chapter course from Hugging Face. You'll start with NLP fundamentals, then move into modern LLM techniques: tokenization, fine-tuning, reasoning models, and building shareable demos. By the end, you'll know how to use pre-trained models, customize them on your own data, and integrate them into real applications. Best if you have some Python experience; the course assumes basic familiarity with machine learning. Fair warning: LLM chapters move fast, so budget time for rereading.