42-speaks-dev

Routing classifies an input and directs it to a specialized followup task. This workflow allows for separation of concerns, and building more specialized prompts. Without this workflow, optimizing for one kind of input can hurt performance on other inputs.

The reason the await call is needed in copyScore(from:) is central to the reasons actors are needed at all.

You see, rather than just letting us poke around in an actor’s mutable state, Swift silently translates that request into what is effectively a message that goes into the actor’s message inbox: “please let me know your score as soon as you... See more

For many applications, however, optimizing single LLM calls with retrieval and in-context examples is usually enough.

This workflow is particularly effective when we have clear evaluation criteria, and when iterative refinement provides measurable value. The two signs of good fit are, first, that LLM responses can be demonstrably improved when a human articulates their feedback; and second, that the LLM can provide such feedback. This is analogous to the iterative... See more

Parallelization is effective when the divided subtasks can be parallelized for speed, or when multiple perspectives or attempts are needed for higher confidence results.