What I Learned

Today’s session introduced me to Retrieval-Augmented Generation (RAG) and why it’s becoming essential in AI. The focus was on understanding the limitations of plain language models (LLMs) and how RAG helps overcome them.

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What is RAG?

Retrieval-Augmented Generation (RAG) is a technique that combines:

Retrieval → fetching relevant, external information (from documents, databases, or the web).

Generation → using a language model (LLM) to produce coherent, context-aware responses.

The term “ Augmented ” means the LLM’s generation process is enhanced by the retrieved information — instead of relying only on its internal training data, it’s augmented with fresh, factual context from external sources.

Together, RAG helps produce outputs that are more factual, relevant, and up-to-date by grounding responses in retrieved information.

Key Concepts:

Language Models (LLMs) generate text by predicting the next word based on learned patterns and context.

They use probabilities and context to…