How Anthropic Is Reinventing RAG Systems with Contextual Retrieval

Anthropic is redefining Retrieval-Augmented Generation (RAG) systems by addressing one of their most persistent limitations: lack of context. Traditional RAG pipelines rely on semantic similarity and keyword matching to retrieve relevant information chunks, but they often miss critical details hidden in surrounding content. Anthropic’s new approach—built on contextual embeddings and chunk-aware prompting—improves precision, reduces retrieval […]

Visualizing Chunking Impacts in Agentic RAG with Agno, Qdrant, RAGAS and LlamaIndex

In the AI Agents world of Retrieval-Augmented Generation (Agentic-RAG), one challenge that persists is how Agents chunk our source documents to optimize response accuracy and relevance. This blog series dives into how different chunking strategies — Fixed, Semantic, Agentic, and Recursive Chunking— impact the performance of Agentic RAG systems. Using Agno for creating agent and orchestration and […]