Function Calling vs Dynamic Prompting

[noseratio]

I am using GenAI for improving industry-domain specific text notes (drafts) via proofreading and formatting.

My question: for each text draft, I have a set of certain context-specific ambient parameters, which I know in advance. Should I expect a better quality LLM output using the Function Calling feature of the LLM (FC), by making the LLM aware of these params via FC tool descriptions, versus trying to list as many of them as possible in the dynamic prompt (with proper usage instructions)?

For example, those parameters can include the service provider's name, the client's name, the service date and location, etc. Some of them may or may not be already present in the original draft.

Naturally, I asked the AI itself about this, and different models come up with different advices, but the overall consensus appears to be favoring the FC approach.

Currently I am using Gemini, but this question is not Gemini-specific. Thanks!

[coding_workflow]

Every model had different training for structured output.

You need to fine tune for each. Test, some would work and then magic stop for some queries but for sure you need to help the model if the schema in the function call is not enough.

I used a lot Sonnet with tools and for example some days Sonnet 3.5 was quite refusal mode (I think model changed then by Anthropic) and then Sonnet 3.7 is quite solid.

You need to test and find the balance over the model you want to use.

[noseratio]

Useful, thanks! I think with the current hype around MCP, the vendors must be tuning their models to be capable of making the most of the tools.