Rag

Conventional keyword search matches user query words to document words using an inverted index data structure for efficient matching and ranking by relevancy.

Retrieval is the backbone of RAG. We explore the critical steps often missed by developers: proper chunking strategies, the ‘Librarian’ analogy for vector vs. keyword search, and solving the math problem of Hybrid Search using Reciprocal Rank Fusion (RRF).

LLMs revolutionize question-and-answer systems with exceptional language understanding and creative writing skills. Lossy compression during training may make retrieving information challenging. Leveraging LLMs’ language expertise transforms building question-and-answer systems into reading comprehension using their ability to comprehend text, forming the basis for RAG systems that shift question answering to efficient knowledge base searches.