For AI agents: a documentation index is available at the root level at /llms.txt and /llms-full.txt. Append /llms.txt to any URL for a page-level index, or .md for the markdown version of any page.
Welcome to the tutorial on Agentic RAG with Cohere!
Retrieval Augmented Generation (RAG) is a technique that gives LLMs the capability to ground their responses in external text data, making the response more accurate and less prone to hallucinations.
However, a standard RAG implementation struggles on more complex type of tasks, such as:
When it has to search over diverse set of sources
When the question requires sequential reasoning
When the question has multiple parts
When it requires comparing multiple documents
When it requires analyzing structured data
In an enterprise setting where data sources are diverse with non-homogeneous formats this approach becomes even more important. For example, the data sources could be a mix of structured, semi-structured and unstructured data.
This is where agentic RAG comes into play, and in this tutorial, we’ll see how agentic RAG can solve these type of tasks.
Concretely, this is achieved using the tool use approach. Tool use allows for greater flexibility in accessing and utilizing data sources, thus unlocking new use cases not possible with a standard RAG approach.
This tutorial is split into six parts, with each part focusing on one use case:
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