Prompt Engineering and LLM Behavior

The throughline that's been developing since late March starts with a simple, slightly embarrassing observation: messy context outperforms clean prompts. Not marginally. Noticeably. A user on r/ChatGPT reported on March 22nd that dumping a wall of raw, unorganised information into ChatGPT produced better outputs than a carefully structured, logically sequenced prompt. This ran directly against everything the LinkedIn prompt-engineering crowd had spent two years evangelising. Six days later, someone on the same forum crystallised why the influencer advice might be actively counterproductive — clean prompts, the argument went, strip out the contextual noise that large language models actually use to orient themselves. You're not helping the model; you're starving it. Around the same time, a user published a prompt that claimed to map their entire personality across 39 traits and 6 layers, which sounds like pseudoscience but points at something real: people are now using these systems for deep self-modelling, not just task completion. ChatGPT even wrote itself a personal ad, which is either a charming curiosity or an early sign that the model's self-representation capabilities are being probed in ways that will matter later. By April, the conversation had shifted toward meta-critique. A prompt-scoring skill appeared on r/LocalLLaMA that would evaluate and repair your prompts automatically — the community had reached the point of building tooling to audit its own practices. A seven-year software engineer posted a blunt guide for vibe coders on turning AI-generated output into something portfolio-worthy, acknowledging the slop problem directly rather than pretending it away. And two separate "Prompt of the Day" posts targeted a single anxiety: are you losing skills you used to have? One tracked professional skill decay, another specifically asked whether AI dependency was making developers worse at coding. The community, in other words, is now asking whether the tools it's optimising are optimising it back — and not in a good direction.

If confirmed, here is what this means. The foundational assumption of prompt engineering as a discipline — that clarity, structure, and precision are the primary levers — may be partially wrong, or at least incomplete. If messy context genuinely outperforms clean prompts for complex tasks, then the enormous amount of time people have invested in learning to write "better" prompts has been pointed at the wrong variable. The practical implication is immediate: knowledge workers who've been refining their prompt syntax should instead be thinking about context richness, about giving the model more of their actual situation rather than a polished version of it. The second-order effect is more uncomfortable. If AI systems perform better with more of your real context, and people are already using them for personality mapping and quasi-therapeutic conversations, the incentive structure pushes toward deeper disclosure to a commercial system that has no fiduciary obligation to you. Meanwhile, the skill decay signals aren't abstract — developers are noticing it in themselves, which means it's moved from theoretical concern to lived experience. Tools that make you better at using them while making you worse at the underlying skill are a particular kind of trap, and the community is only just beginning to name it.

Watch for any structured research — academic or otherwise — that tests prompt clarity versus context density across task types, and for whether the major AI labs update their official prompting guidance in ways that quietly acknowledge what these practitioners have been finding.

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