A New Course on Retrieval Augmented Generation (RAG) Is Live!

Why It Matters

RAG equips enterprises to embed up‑to‑date, proprietary knowledge into LLMs, turning generic AI into a strategic, revenue‑generating asset.

Key Takeaways

• RAG pairs LLMs with trusted databases for domain knowledge.
• RAG underpins AI summaries in web searches and IDE code agents.
• Course requires coding basics, no prior AI experience needed.
• Learners gain theory and hands‑on labs to build RAG applications.
• Enables enterprises to launch customized, data‑driven chatbots quickly.

Summary

The video announces a brand‑new online course that teaches Retrieval Augmented Generation (RAG), the technique that couples large language models with external knowledge bases to boost enterprise‑grade performance.

Instructor Zan Hassani explains that RAG is already powering everyday tools—from AI‑enhanced search snippets to code‑assistant agents in modern IDEs—by feeding LLMs up‑to‑date, domain‑specific data. The curriculum covers both the theoretical underpinnings and practical labs, targeting developers comfortable with code but new to AI.

Hassani emphasizes, “If you want a personal assistant that knows your calendar or a chatbot that can speak authoritatively about your products, you need RAG.” He cites examples such as personalized search summaries and real‑time code generation as proof points.

By democratizing RAG skills, the course aims to accelerate adoption of customized AI solutions across industries, reducing time‑to‑market for data‑driven applications and giving businesses a competitive edge.

Original Description

Learn more: https://bit.ly/3GKWqrY

We’re thrilled to announce the launch of a new course: Retrieval Augmented Generation (RAG), taught by AI engineer Zain Hasan, and available on Coursera.

This hands-on course shows you how to build production-ready RAG systems, connecting language models to external data sources to improve accuracy, reduce hallucinations, and support real-world use cases.

You'll move beyond prototype-level LLM apps to build full RAG pipelines that are scalable, adaptable, and grounded in real context. In detail, you’ll:

- Combine retrievers and LLMs using tools like Weaviate, Together.AI, and Phoenix

- Apply effective retrieval such as keyword search, semantic search, and metadata filtering, and know when to use each

- Evaluate system performance, balance cost-speed-quality tradeoffs, and prep your pipeline for deployment

You’ll work with real-world datasets from domains like healthcare, media, and e-commerce, gaining a practical foundation and engineering judgment you can apply in production settings.

This course is designed for software engineers, ML practitioners, and technical professionals building with LLMs. If your applications require accuracy, traceability, and relevance, this course will show you how to get there with RAG.

Enroll now: https://bit.ly/3GKWqrY