Introduction to RAG for LLMs: Sparse (Lexical) RAG and Dense RAG (Semantic Vector Search)

📰 Dev.to · Jun Bae

Learn about RAG for LLMs, including Sparse (Lexical) RAG and Dense RAG (Semantic Vector Search), to improve your understanding of AI and machine learning

intermediate Published 25 Apr 2026

Action Steps

Read about the basics of RAG and its types, including Sparse (Lexical) RAG and Dense RAG (Semantic Vector Search)
Understand how LLMs store information within their parameters and how RAG can be used to improve their performance
Explore the applications of RAG in natural language processing and information retrieval
Implement RAG using Python and popular libraries such as Transformers and Faiss
Evaluate the performance of RAG-based models using metrics such as accuracy and recall

Who Needs to Know This

This article is relevant for AI engineers, machine learning researchers, and software developers who want to improve their knowledge of RAG and LLMs. It can help them design and implement more efficient AI systems.

Key Insight

💡 RAG can be used to improve the performance of LLMs by providing them with external knowledge and reducing their reliance on internal parameters