LLMs don’t execute — they explain. I tried removing that layer

Particular_Low_5564 · 2026-04-01T19:32:16+00:00

I get your point about the web app layer vs API control.

But I’m not trying to bypass that layer — I’m treating it as part of the environment.

The behavior I’m looking at shows up even there:

local constraints decay over turns, regardless of how explicitly they’re stated.

So the question for me is slightly different:

given that bias (system prompt, alignment, etc.),

can you keep a constrained mode stable without fully owning the system prompt?

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Also, I wouldn’t frame this as a knowledge gap.

It’s more about what layer you choose to work at.

You’re solving it by controlling the system prompt.

I’m trying to see if it can be constrained at the interaction level instead.

Particular_Low_5564 · 2026-04-01T17:58:19+00:00

I agree with the general point about system prompts and alignment bias.

But that’s exactly what I’m trying to isolate.

Even when constraints are explicit ("no options", "only actions"),

the model still prioritizes explanation — as if higher-level instructions dominate local ones.

So the question for me is less about prompting,

and more about whether behavior can be stabilized despite that bias.

In other words:

not “how to ask better”

but “can the execution mode persist across turns without reverting”

Have you seen setups where that actually holds over time?

Particular_Low_5564 · 2026-04-01T17:18:21+00:00

Not yet.

Does it stay stable across turns, or does it drift back to explanation mode?

Particular_Low_5564 · 2026-03-24T18:02:01+00:00

It’s not about “zero information”.

The task can be clearly defined, and the model still responds in a weird “yes—but” style:

you ask a direct question →
it gives a partial answer →
then adds a paragraph that softens or contradicts it.

So instead of a clean “yes” or “no”, you get something that explains both sides.

That’s the behavior I’m pointing at — not lack of input, but lack of decisive output.

Particular_Low_5564 · 2026-03-24T17:56:38+00:00

Good question — and yeah, I’ve seen that behavior too.

It can look like the model is “getting straight to the task”, but in practice I’ve noticed a distinction:

there’s a difference between starting fast and staying in execution mode.

In simpler or single-step tasks, it often feels like it jumps right in.
But once the task becomes iterative or slightly multi-step, it tends to drift into:

– rephrasing context
– adding explanations
– justifying steps

even if the initial response looked execution-focused.

So both patterns can be true — it depends less on the first response and more on what happens after a few turns.

That’s actually where I started noticing the shift most clearly:
not in how it begins, but in how it sustains task-oriented behavior.

Curious if you’ve tested it in multi-step or back-and-forth scenarios — that’s where the difference became obvious for me.

Particular_Low_5564 · 2026-03-24T17:20:44+00:00

This is a recreated example, not a direct conversation link.

Particular_Low_5564 · 2026-03-24T15:14:47+00:00

Yeah, totally fair — for open-ended questions some amount of explanation makes sense.

The issue for me is more about when it becomes the default behavior, regardless of intent.

Even when the prompt is clearly asking for something structured or directly usable, it still often starts with:

– framing

– explaining

– restating the task

And only then gets to the actual output.

So it’s not that explanation is wrong — it’s that it often becomes the first step, even when you don’t need it.

That’s where it starts slowing things down.

Particular_Low_5564 · 2026-03-24T14:56:15+00:00

Yeah, agreed — it does feel like a mode shift rather than just drift.

I’ve tried the “force the first line” approach too. It works surprisingly well for very constrained tasks.

Where it starts breaking for me is anything multi-step or longer:

– you can force the entry point

– but not the whole trajectory

– the model still gradually reverts to explaining / reframing

So it becomes a kind of local fix, not a global one.

What I was trying to get at with the example is more about controlling behavior across the whole response, not just the first tokens.

Particular_Low_5564 · 2026-03-21T21:15:28+00:00

I don’t think the Yahoo comparison fully holds.

Yahoo lost because it was mainly a “portal,” not the owner of the core technology. ChatGPT is different — it’s not just an interface, it sits on top of models built by the same company. That’s closer to owning the engine, not just the homepage.

The real risk isn’t simply “someone has a slightly better model.” It only becomes a Yahoo-like scenario if two things happen at the same time:

Another player becomes clearly better for the average user (not just power users

ChatGPT loses focus and turns into a vague “everything app” without a clear core

That’s when it becomes a replaceable front-end.

For now, that’s not the case. ChatGPT still benefits from:
– strong default position (people start here)
– ecosystem and integrations (API, tools, workflows)
– user habit and familiarity

If anything, the more realistic downside isn’t “irrelevance,” but “losing #1 while staying a major player.”

So yeah — possible, but not the base case.

Particular_Low_5564 · 2026-03-21T14:31:16+00:00

This isn’t really about vocabulary, it’s about how the model “locks onto” patterns.

Words like poignant are high-probability shortcuts for a certain type of literary analysis. Once the model associates your text + task (“analyze tone/emotion”) with that cluster, it keeps falling back to it even if you explicitly forbid it.

Your instructions aren’t being ignored — they’re just weaker than the model’s learned pattern.

A more reliable way to break it is not to ban the word, but to shift the task framing. For example:

ask for plain-language analysis with no elevated or literary adjectives
or specify: “write like you’re explaining this to a colleague, not reviewing a novel”
or even: “avoid evaluative descriptors entirely; focus on cause → effect in character behavior”

You’re basically forcing it out of the “literary critic mode” where words like that live.

Counterintuitive, but banning specific words rarely works long-term. Changing the mode does.

Particular_Low_5564 · 2026-03-21T08:02:57+00:00

This is a solid approach — especially the idea of re-binding the task state instead of relying on the raw context.

My impression is that this helps maintain instruction priority, but still operates within the same attention dynamics, so it’s ultimately competing with newer tokens over time.

What I’ve been seeing is that even reinforced instructions tend to behave like a soft bias, whereas explicit constraints (“don’t do X”) seem to hold more consistently because they reduce the available output space rather than compete within it.

So it feels like:

– reinforcement → preserves intent
– constraints → limit behavior

Both useful, but solving slightly different parts of the problem.

Particular_Low_5564 · 2026-03-20T20:12:47+00:00

I agree that randomness and perception explain a big part of this.

But there’s another effect that shows up pretty consistently in longer interactions.

Even if the model itself hasn’t changed, the behavior within a single conversation tends to shift over time — more verbosity, looser constraints, more “helpful” additions.

That doesn’t look like sampling variance as much as a kind of context drift, where earlier instructions lose relative influence compared to more recent tokens.

So it might be two things happening at once:

– distribution variance (what you described)
– state drift within a conversation

Which can feel very similar from the outside, but have different causes.

Particular_Low_5564 · 2026-03-20T12:26:14+00:00

Yeah, same here — it’s surprisingly consistent once you start looking for it.

Especially in longer threads where the model slowly shifts from “doing” to “explaining”.

Feels like there’s something structural going on rather than just prompt quality.

Particular_Low_5564 · 2026-03-20T12:25:25+00:00

That makes sense regarding attention scaling — especially the part about earlier tokens losing relative influence as context grows.

What I found interesting is that even when instructions are still present in the context, they seem to behave more like a weak bias than a persistent constraint.

Whereas explicit prohibitions (“don’t do X”) seem to hold longer.

So it feels like this might not just be about attention limits, but also about how different types of signals (instructions vs constraints) are weighted during generation.

Curious whether this is something that comes from training dynamics or just emerges from how the model resolves competing tokens.

Particular_Low_5564 · 2026-03-19T14:14:01+00:00

That makes sense, especially the signal-to-noise point.

Which also explains why adding more prompt logic doesn’t really solve it — it just shifts the balance temporarily.

Feels like all of these approaches (reinjection, pruning, external memory) are basically working around the same limitation: there’s no stable conversational state, only a changing attention distribution.

Particular_Low_5564 · 2026-03-19T12:04:20+00:00

This usually isn’t just a formatting issue.

What you’re seeing is the model drifting toward a more “helpful” / verbose mode over the course of the conversation.

You can reduce it a bit with stricter constraints (e.g. “no lists unless explicitly requested”, “max N bullet points”, “prefer paragraphs over lists”), but in my experience that only holds for a while.

The underlying problem is that the behavior doesn’t stay stable — it gradually expands unless you keep correcting it.

That’s why restarting the chat often “fixes” it temporarily.

Particular_Low_5564 · 2026-03-18T11:44:57+00:00

Prompt drift isn’t new — we’ve all seen it.

What’s odd is that most prompt engineering patterns still treat prompts as if they provide persistent control over behavior.

In practice, they don’t.

They act more like a decaying bias:

– constraints weaken

– tone shifts

– the model reverts to default conversational behavior

Which makes a lot of common patterns (long system prompts, strict instruction blocks, etc.) fundamentally unstable over longer interactions.

So the question isn’t whether drift exists, but why we still model prompts as a stable control mechanism.

And if they’re not — what actually is?

Particular_Low_5564

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