[ursaguild]

Ingesting documents and using natural language to search your org docs with an internal assistant sounds more like a good use case for RAG[1]. Agents are best when you need to autonomously plan and execute a series of actions[2]. You can combine the two but knowing when depends on the use case.

I really like the OpenAI approach and how they outlined the thought process of when and how to use agents.

[1] https://www.willowtreeapps.com/craft/retrieval-augmented-gen...

[2] https://www.willowtreeapps.com/craft/building-ai-agents-with...

[ednite]

Interesting, and thanks for explanations.

In this case, the agent would also need to learn from new events, like project lessons learned, for example.

Just curious: can a RAG[1] system actually learn from new situations over time in this kind of setup, or is it purely pulling from what's already there?

[ursaguild]

Especially with a client, consider the word choices around "learning". When using llms, agents, or rag, the system isn't learning (yet) but making a decision based on the context you provide. Most models are a fixed snapshot. If you provide up to date information, it will be able to give you an output based on that.

"Learning" happens when initially training the llm or arguably when fine-tuning. Neither of which are needed for your use case as presented.

[mousetree]

You can ingest new documents and data into the RAG system as you need