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RAG Chunking for Knowledge Retrieval

11 posts · Apr 2024 – Sep 2026

What this trail covers

Cohere's Coral platform, a Knowledge Assistant for Enterprises, leverages Retrieval Augmented Generation (RAG) for enterprise knowledge retrieval. This capability is enhanced by advanced chunking strategies for optimized document segmentation, ensuring semantic coherence and contextual relevance for improved model processing and retrieval accuracy. Command R and Command R+ further enhance RAG by providing a more powerful LLM for generation and improved RAG capabilities, including tool use and citations. Compass Cloud offers a managed retrieval platform for developers building AI applications with enterprise data, providing connectors, parsing, embedding, indexing, retrieval, reranking, and governance. It supports both agentic and conventional search applications, consolidating the core retrieval stack into an integrated enterprise platform. Compass API and Model Context Protocol (MCP) server are available for custom RAG and agentic retrieval respectively.

All Cohere trails
  1. Compass is coming to the cloud | CohereThis post announces the private beta of Compass Cloud, a managed offering of Cohere's retrieval platform. It details how Compass addresses open problems in enterprise search, including token economics, agentic access patterns, and a fragmented retrieval stack. The post outlines Compass's full-stack architecture, covering data connection, parsing, embedding, indexing, retrieval, reranking, and governance. It highlights premium retrieval accuracy with a benchmark comparison and introduces two integration methods: Compass API for custom RAG/search and Model Context Protocol (MCP) for agentic retrieval. The post also mentions the availability of self-hosted deployments for privacy-constrained projects.
  2. Agentic RAG: A Practical Guide for EnterprisesThis post details the application of AI agents to enhance Retrieval Augmented Generation (RAG) systems for enterprises. It introduces the concept of 'Agentic RAG,' which integrates intent recognition and multi-step retrieval processes to improve how chatbots understand user goals and context. The system leverages agents to break down complex queries into sub-tasks and orchestrate information retrieval from various sources, leading to more accurate and context-aware results. This builds upon existing RAG capabilities by adding an agentic layer for more sophisticated query handling and information access.
  3. Chunking for RAG: Enhance Enterprise Knowledge Retrieval with Generative AIThis post details advanced chunking strategies for optimized document segmentation in RAG systems, focusing on ensuring semantic coherence and contextual relevance for improved model processing and retrieval accuracy. It highlights how these techniques enhance enterprise knowledge retrieval.
  4. Introducing Chat with Retrieval-Augmented Generation (RAG)This post introduces Chat with RAG, enabling users to build conversational AI applications that can access and retrieve information from their own data sources. It details the integration of RAG into a chat interface, allowing for more informed and contextually relevant responses. The system leverages advanced chunking strategies for optimized document segmentation, ensuring semantic coherence and contextual relevance for improved model processing and retrieval accuracy. Command R and Command R+ further enhance RAG by providing a more powerful LLM for generation and improved RAG capabilities, including tool use and citations.
  5. RAG is here to stay: Four reasons why large context windows can't replace itThis post argues that while large context windows in LLMs are powerful, they cannot fully replace Retrieval Augmented Generation (RAG) for effective knowledge retrieval. It emphasizes that RAG's ability to precisely retrieve relevant information and ground responses in specific data sources remains critical. The post implicitly reinforces the importance of RAG components like chunking and retrieval mechanisms, suggesting that the evolution of LLMs will complement, rather than supersede, RAG architectures.
  7. Introducing Coral, the Knowledge Assistant for EnterprisesThis post introduces Coral, a new Knowledge Assistant for Enterprises that leverages Retrieval Augmented Generation (RAG). It highlights the use of advanced chunking strategies for optimized document segmentation to ensure semantic coherence and contextual relevance for improved model processing and retrieval accuracy. The post also mentions that Command R and Command R+ enhance RAG with more powerful LLMs, tool use, and citations.
  8. Command R: RAG at Production ScaleThis post details the production-scale RAG capabilities of Command R, highlighting its ability to handle large volumes of data and complex retrieval tasks efficiently. It emphasizes the model's architecture and design choices that enable robust RAG at scale, including strategies for data ingestion, indexing, and retrieval. The post discusses the trade-offs involved in scaling RAG systems, such as latency, cost, and accuracy, and how Command R addresses these challenges.
  9. Advancing RAG with Command R to Solve Real Business ProblemsThis post details how Command R's enhanced RAG capabilities, including tool use and citations, are applied to solve real business problems. It highlights practical applications such as automating fork maintenance with AI agents, improving enterprise search reranking with Rerank 3.5, and enabling cross-lingual classification with the Multilingual Text Understanding Model. The post emphasizes the integration of these advanced RAG features into real-world scenarios, showcasing their effectiveness in addressing specific business challenges.
  10. Augmenting Personal Knowledge ManagementThis post details how to augment personal knowledge management by leveraging RAG for personal knowledge bases, using techniques like document chunking and embedding for effective retrieval and synthesis of information.