Episode Details

When Daniel wrote in about the "strange paucity" of text-in, text-out AI tools, he tapped into a fundamental gap in how LLMs are built and deployed. This episode unpacks the difference between conversational workflows (multi-turn, stateful, context-accumulating) and transformation workflows (single-turn, stateless, input-to-output mapping). We trace how the conversation assumption is baked into every layer of the stack — from SDKs to API design to fine-tuning — and why that makes pure transformation pipelines actively harder to build. We explore the cost of dragging bad outputs through context windows, the synchronous versus asynchronous distinction, and why the completions endpoint is treated as the neglected sibling to chat completions. Plus, we look at where model development is heading with instruction-following models and parameter selection, and what tooling exists — or doesn't — for builders who want stateless LLM calls as a first-class primitive.