alt Hacker News>Conclusion
Building powerful and reliable AI Agents is becoming less about finding a magic prompt or model updates. It is about the engineering of context and providing the right information and tools, in the right format, at the right time. It’s a cross-functional challenge that involves understanding your business use case, defining your outputs, and structuring all the necessary information so that an LLM can “accomplish the task."
That’s actually also true for humans: the more context (aka right info at the right time) you provide the better for solving tasks.
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THis.. I was about to make a similar point; this conclusion reads like a job description for a technical lead role where they managed and define work for a team of human devs who execute implementation.
The difference is that humans can actively seek to acquire the necessary context by themselves. They don't have to passively sit there and wait for someone else to do the tedious work of feeding them all necessary context upfront. And we value humans who are able to proactively do that seeking by themselves, until they are satisfied that they can do a good job.
Basically, finding the right buttons to push within the constraints of the environment. Not so much different from what (SW) engineering is, only non-deterministic in the outcomes.
Yeah... I'm always asking my UX and product folks for mocks, requirements, acceptance criteria, sample inputs and outputs, why we care about this feature, etc.
Until we can scan your brain and figure out what you really want, it's going to be necessary to actually describe what you want built, and not just rely on vibes.
Not "more" context. "Better" context.
(X-Y problem, for example.)
I am not a fan of this banal trend of superficially comparing aspects of machine learning to humans. It doesn't provide any insight and is hardly ever accurate.