If you have to deal with what is called #AI in a professional environment these days, you will often come across the term #RAG. These Retrieval-Augmented Generation systems are intended to address many of the weaknesses of Large-Language Models (#LLM).

These are the core elements:

🔍 Retriever

• Fetches relevant documents from external knowledge bases.
• Utilizes vector representations for efficient text search.
• Employs methods like keyword-based search, semantic search, or vector search to find pertinent information.

🧩 Augmentation

• Integrates retrieved data into the model’s input.
• Filters and structures information for relevance.
• Prepares data to optimize the generation process.

💡 Generator

• The Large Language Model (LLM) processes both the query and augmented information.
• Generates responses conditioned on the retrieved data.
• Delivers the final, synthesized response of the RAG system.

RAG systems help create more accurate, contextual and efficient solutions. This makes them more useful in many areas. They are just one step in the increasingly meaningful application of machine intelligence to real-world use cases.