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Agentic Search

4 posts · Nov 2023 – Jul 2025

What this trail covers

Cohere's agentic search capability is enhanced by integrating intent recognition, allowing chatbots to more accurately understand user goals and context. This improves the system's ability to plan and execute multi-step retrieval processes, leading to more sophisticated and context-aware information retrieval. The system leverages agents to understand complex queries, break them down into sub-tasks, and orchestrate the retrieval of information from various sources, leading to more accurate and r

All Cohere trails
  1. From GraphRAG to agentic search: Boost AI retrievalThis post details the evolution of Cohere's retrieval capabilities, moving from GraphRAG to agentic search. It highlights the integration of intent recognition into agentic search to improve user goal understanding and context awareness. The system orchestrates multi-step retrieval processes by breaking down complex queries into sub-tasks, leading to more sophisticated and context-aware information retrieval.
  2. Improve AI Search and RetrievalThis post details the integration of intent recognition into Cohere's agentic search, enabling more accurate understanding of user goals and context. This enhancement allows for improved planning and execution of multi-step retrieval processes, leading to more sophisticated and context-aware information retrieval. The system leverages agents to understand complex queries, break them down into sub-tasks, and orchestrate retrieval from various sources.
  3. Building a Search-Based Discord Bot with Language ModelsThis post details the technical implementation of a Discord bot that leverages language models for a search-based user experience. It describes how to integrate a Discord bot with a language model API to enable natural language querying of information. The bot's architecture involves receiving user messages, processing them with an LLM to understand intent and extract search queries, and then performing a search against a knowledge base. The results are then formatted and returned to the user via Discord. Key technical aspects include prompt engineering for intent recognition and query generation, handling Discord API interactions, and managing the LLM inference process for responsiveness. The post highlights the challenges of balancing LLM capabilities with real-time user interaction and the trade-offs involved in designing a user-friendly, AI-powered search interface within a chat environment.
  4. Turbocharge Your Chatbot with Intent RecognitionThis post details the integration of intent recognition into Cohere's agentic search, enabling chatbots to better understand user goals and context. This enhancement allows for more accurate planning and execution of multi-step retrieval processes, leading to more sophisticated and context-aware information retrieval. The system leverages agents to understand complex queries, break them down into sub-tasks, and orchestrate retrieval from various sources.
  5. Why You Need a Large Language Model for Intent RecognitionThis post details how Large Language Models (LLMs) can be used for intent recognition, a crucial component of agentic search. It explains that LLMs can understand the nuances of user queries, identify the underlying intent, and map it to specific actions or information retrieval tasks. This capability allows for more sophisticated and context-aware interactions with AI systems, moving beyond simple keyword matching to a deeper understanding of user goals. The post highlights the benefits of using LLMs for intent recognition in building more intelligent and responsive AI agents.