alt Hacker News> 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.
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There is only so much you can do with prompts. To go from the 70% accuracy you can achieve with that to the 95% accuracy I see in Claude Code, the context is absolutely the most important, and it’s visible how much effort goes into making sure Claude retrieves exactly the right context, often at the expense of speed.
If someone asked you about the usages of a particular element in a codebase, you would probably give a more accurate answer if you were able to use a code search tool rather than reading every source file from top to bottom.
For that kind of tasks (and there are many of those!), I don't see why you would expect something fundamentally different in the case of LLMs.
> when the "right" format and "right" time are essentially, and maybe even necessarily, undefined, then aren't you still reaching for a "magic" solution?
Exactly the problem with all "knowing how to use AI correctly" advice out there rn. Shamans with drums, at the end of the day :-)
It's called over-fitting, that's basically what prompt engineering is.
Yeah but do we have to make a new buzz word out of it? "Context engineer"
The state of the art theoretical frameworks typically separates these into two distinct exploratory and discovery phases. The first phase, which is exploratory, is best conceptualized as utilizing an atmospheric dispersion device. An easily identifiable marker material, usually a variety of feces, is metaphorically introduced at high velocity. The discovery phase is then conceptualized as analyzing the dispersal patterns of the exploratory phase. These two phases are best summarized, respectively, as "Fuck Around" followed by "Find Out."
It’s just AI people moving the goalposts now that everyone has realised that “prompt engineering” isn’t a special skill.
This is like telling a soccer player that no change in practice or technique is fundamentally different than another, because ultimately people are non-deterministic machines.
It's magical thinking all the way down. Whether they call it now "prompt" or "context" engineering because it's the same tinkering to find something that "sticks" in non-deterministic space.