That is prompting. It's all a prompt going in. The parts you see or don't see as an end user is just UX. Of course when you obscure things, it changes the UX for the better or the worse.

The central argument is that the importance of prompt "tricks"—essentially empirical workarounds for current LLM limitations will decline as the technology matures.

The truly valuable and future-proof skill is "context engineering". This focuses on providing the LLM with the information required to reason through the task at hand. Although current LLMs present a trade-off between the size of the context and the quality of the output, this is a constraint that we can expect to lessen with future advancements.

Premises and conclusions.

Prompts and context.

Hopes and expectations.

Black holes and revelations.

We learned to write and then someone wrote novels.

Context, now, is for the AI, really, to overcome dogmas recursively and contiguously.

Wasn't that somebody's slogan someday in the past?

Context over Dogma

I’m curious how this applies to systems like ChatGPT, which now have two kinds of memory: user-configurable memory (a list of facts or preferences) and an opaque chat history memory. If context is the core unit of interaction, it seems important to give users more control or at least visibility into both.

I know context engineering is critical for agents, but I wonder if it's also useful for shaping personality and improving overall relatability? I'm curious if anyone else has thought about that.

There is no need to develop this ‘skill’. This can all be automated as a preprocessing step before the main request runs. Then you can have agents with infinite context, etc.

The model starts every conversation as a blank slate, so providing a thorough context regarding the problem you want it to solve seems a fairly obvious preparatory step tbh. How else is it supposed to know what to do? I agree that "prompt" is probably not quite the right word to describe what is necessary though - it feels a bit minimal and brief. "Context engineering" seems a bit overblown, but this is tech. and we do a love a grand title.

What’s it going to be next month?

So then for code generation purposes, how is “context engineering” different now from writing technical specs? Providing the LLMs the “right amount of information” means writing specs that cover all states and edge cases. Providing the information “at the right time” means writing composable tech specs that can be interlinked with each other so that you can prompt the LLM with just the specs for the task at hand.

I agree with everything in the blog post. What I'm struggling with right now is the correct way of executing things the most safe way but also I want flexibility for LLM. Execute/choose function from list of available fns is okay for most use cases, but when there is something more complex, we need to somehow execute more things from allowed list, do some computations in between calls etc.

This is just another "rebranding" of the failed "prompt engineering" trend to promote another borderline pseudo-scientific trend to attact more VC money to fund a new pyramid scheme.

Assuming that this will be using the totally flawed MCP protocol, I can only see more cases of data exfiltration attacks on these AI systems just like before [0] [1].

Prompt injection + Data exfiltration is the new social engineering in AI Agents.

[0] https://embracethered.com/blog/posts/2025/security-advisory-...

[1] https://www.bleepingcomputer.com/news/security/zero-click-ai...