The new skill in AI is not prompting, it's context engineering

>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.

Definitely mirrors my experience. One heuristic I've often used when providing context to model is "is this enough information for a human to solve this task?". Building some text2SQL products in the past it was very interesting to see how often when the model failed, a real data analyst would reply something like "oh yea, that's an older table we don't use any more, the correct table is...". This means the model was likely making a mistake that a real human analyst would have without the proper context.

One thing that is missing from this list is: evaluations!

I'm shocked how often I still see large AI projects being run without any regard to evals. Evals are more important for AI projects than test suites are for traditional engineering ones. You don't even need a big eval set, just one that covers your problem surface reasonably well. However without it you're basically just "guessing" rather than iterating on your problem, and you're not even guessing in a way where each guess is an improvement on the last.

edit: To clarify, I ask myself this question. It's frequently the case that we expect LLMs to solve problems without the necessary information for a human to solve them.

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.

We shouldn't but it's analogous to how CPU usage used to work. In the 8 bit days you could do some magical stuff that was completely impossible before microcomputers existed. But you had to have all kinds of tricks and heuristics to work around the limited abilities. We're in the same place with LLMs now. Some day we will have the equivalent of what gigabytes or RAM are to a modern CPU now, but we're still stuck in the 80s for now (which was revolutionary at the time).

> Building powerful and reliable AI Agents is becoming less about finding a magic prompt or model updates.

Ok, I can buy this

> It is about the engineering of context and providing the right information and tools, in the right format, at the right time.

when the "right" format and "right" time are essentially, and maybe even necessarily, undefined, then aren't you still reaching for a "magic" solution?

If the definition of "right" information is "information which results in a sufficiently accurate answer from a language model" then I fail to see how you are doing anything fundamentally differently than prompt engineering. Since these are non-deterministic machines, I fail to see any reliable heuristic that is fundamentally indistinguishable than "trying and seeing" with prompts.