Orchestration of multiple data sources and prompts is not trivial. Systems attempt to detect the user’s intent and route the workflow through multiple prompts, but that increases the surface area of failure cases. Planning and multi-turn workflows are also sought after but prove to be even harder to steer. As many developers told us, it goes “off the rails
Best practices on how to work with LLMs was sought after by many of the developers. They resort to following Twitter hashtags or reading arXiv papers to learn, but it doesn’t scale and they don’t know which resources are good. The field is moving fast, and it requires developers to “throw away everything that they’ve learned and rethink it
Developers are having to learn and compare many new tools rather than focusing on the customer problem. They then have to glue these tools together
When the tool lives where the knowledge already is, experimentation with knowledge lives on the surface area of something familiar — lowering the barrier to meaningful use. enzyme
