Retrieval Augmented Generation (RAG)
Understand the RAG architecture and how to combine retrieval systems with LLMs for informed responses.
What is RAG?
Welcome to Retrieval Augmented Generation (RAG)! This lesson introduces a powerful technique for making Large Language Models (LLMs) more accurate and reliable.
RAG combines the vast knowledge of LLMs with up-to-date, external information. It's like giving an LLM an open book during a tough exam!
Why Do We Need RAG?
Vanilla LLMs, while impressive, have some limitations:
- Hallucinations: They can sometimes generate factually incorrect or nonsensical information.
- Outdated Knowledge: Their knowledge is limited to their training data cut-off date.
- Lack of Specificity: They may not know niche or private domain-specific facts.
- No Citations: They can't easily tell you where their information comes from.
All lessons in this course
- Prompting with External Data
- Retrieval Augmented Generation (RAG)
- Vector Databases for Prompting