Retrieval Augmented Generation

Retrieval-Augmented Generation (RAG) enhances LLM text generation using external knowledge

Editor4 weeks ago4 weeks ago08 mins

Retrieval-Augmented Generation (RAG) enhances LLM text generation by incorporating external knowledge sources, making responses more accurate, relevant, and up-to-date. RAG combines an information retrieval component with a text generation model, allowing the LLM to access and process information from external databases before generating text. This approach addresses challenges like domain knowledge gaps, factuality issues, and hallucinations often…

The Top Open-Source RAG Frameworks to Know in 2025: Build Smarter AI with Real-World Context

Editor1 month ago07 mins

Retrieval-Augmented Generation (RAG) is quickly redefining how we build and deploy intelligent AI systems. It isn’t a replacement for large language models (LLMs)—it’s the missing piece that makes them useful in real-world settings. With hallucinations, outdated knowledge, and limited memory being persistent LLM issues, RAG introduces a smarter approach: retrieve factual information from reliable sources,…