Learn RAG is a 94-minute intermediate course on building LLM-powered applications with Retrieval-Augmented Generation. Students start by understanding embeddings and why they are crucial to improving AI accuracy. The course then moves into chunking documents, generating embeddings, and storing them in vector databases such as Supabase. Learners practice similarity search and combine retrieved results with the OpenAI ChatCompletions API to create smarter, context-aware responses. The capstone project is a proof-of-concept AI Movie Recommendation engine that demonstrates RAG in action.
Hackr.io’s learners value Scrimba because of its unique interactive coding format. Instead of just watching videos, students code directly inside the platform, pause instructors, edit code, and test changes on the spot. This makes the learning experience active rather than passive. Combined with practical projects and an engaging style, Scrimba has earned the trust of the Hackr.io community as a reliable way to master modern tech skills quickly. The course is taught by Guil Hernandez, a web developer and educator known for making technical topics approachable. Guil brings enthusiasm and clarity to his lessons, helping students build confidence as they tackle advanced concepts like RAG.
Retrieval-Augmented Generation is a technique that boosts the reliability of large language models by grounding them in external knowledge sources. Instead of relying only on pre-trained parameters, RAG retrieves the most relevant information from a vector database and uses it to shape the model’s output. This makes applications more accurate, reduces hallucinations, and ensures responses are up to date with real-world data.