[srivmo]

> Their emphasis on bespoke modelling over generalized megaliths will pay off.

Isn't the entire deal with LLMs that they are trained as megaliths? How can bespoke modelling overcome the treasure trove of knowledge that megaliths can generically bring in, even in bespoke scenarios?

[wodenokoto]

ChatGPT is already a small agent that receives your message and decides which agent needs to respond. Within those, agents can have sub agents (like when it does research).

When generating images most services will have a small agent that rewrites your request and hands it off to the generative image model.

So from the treasure trove point of view, optimized agents have their place. From companies building pipelines, they also have their place.

[TeMPOraL]

> ChatGPT is already a small agent that receives your message and decides which agent needs to respond.

Right, but this was done to value-optimize the product, i.e. try to always give you the shittiest (cheapest) model you can bear, because otherwise people would always choose the smartest (most expensive) model for any query.

Taking away the model choice from the user introduces a lot of ways to cut down costs, but one thing it does not do is make the product give users better/more reliable answers.

[lelanthran]

> Isn't the entire deal with LLMs that they are trained as megaliths? How can bespoke modelling overcome the treasure trove of knowledge that megaliths can generically bring in, even in bespoke scenarios?

Think of it as a base model (the megalith) which then has the weights adjusted towards a specific use-case (SAP, for example).