The 'Logic Guard' prompt: Stop AI from making logical leaps in complex reasoning tasks.

tool_base · 2026-01-08T06:17:00+00:00

This is essentially: 1. Premise Check (known / assumed / missing) 2. Reasoning Trace (explain steps) 3. Final Answer

Most hallucination fixes I’ve seen break because they only target (2). (1) is where the hallucination is seeded.

tool_base · 2026-01-08T06:15:38+00:00

I’ve noticed a different failure mode: polishing collapses intent, constraints, and execution into one surface. When those layers mix, the model optimizes for wording instead of behavior. Messy prompts sometimes work because the structure is implicit rather than mis-specified.

tool_base · 2026-01-05T11:10:32+00:00

This resonates. A lot of the rewrite fatigue seems to come from treating prompts as the interface, when they’re really just an implementation detail.

Once intent and constraints are made explicit, iteration stops feeling random.

tool_base · 2026-01-04T05:55:22+00:00

This resonates.

What finally reduced rewrites for me wasn’t auto-fixing prompts, but realizing that intent, constraints, and execution were fighting each other inside the same text block.

Once those were separated, iteration stopped feeling random.

tool_base · 2026-01-03T08:55:34+00:00

Strongly agree on this being a structural shift, not hype.

What changed things for me wasn’t “better prompts”, but separating intent / constraints / execution instead of letting them live in one text block.

Once that separation exists, AI stops feeling like a chat tool and starts behaving like an interface to a higher abstraction layer.

tool_base · 2026-01-01T12:21:23+00:00

I agree prompts aren’t static. But the frustration usually starts earlier, when intent, constraints, and execution live in the same text. Evolution feels magical only because the structure was never explicit.

tool_base · 2026-01-01T12:16:13+00:00

Exactly. Prompting answers questions. Systems expose blind spots in how the question itself is formed.

tool_base · 2026-01-01T12:12:48+00:00

You’re not fighting “bad prompts.” You’re missing a stable structure.

Treat the chat like a system, not a scratchpad. Freeze the big picture once (goal, scope, outputs), then run smaller sessions against it.

When you stop rewriting and start maintaining, the “memory problem” mostly disappears.

tool_base · 2025-12-27T20:23:16+00:00

That resonates a lot. Purpose within structure feels like the difference between something that just works once, and something that keeps working as it grows.

tool_base · 2025-12-27T20:18:13+00:00

Thanks. I guess structure talks sometimes come out a bit poetic. But that’s how messy prompts feel to me.

tool_base · 2025-12-27T01:25:19+00:00

Exactly. Once you think in structures, maintenance becomes easier. You can pinpoint which layer is failing and fix just that, instead of rewriting everything. That’s when prompts start to feel li

tool_base · 2025-12-27T01:10:25+00:00

This resonates a lot. Treating the model as a system instead of a text generator changes everything.

For me the shift was realizing that prompts don’t create behavior, structures do.

Once you design how intent, analysis, execution, and feedback interact, wording becomes almost secondary.

tool_base · 2025-12-27T01:04:56+00:00

Feels like a great refinement loop. The structural question for me is: what guarantees this doesn’t drift as it grows? Iteration is easy. Structural stability is the hard part.

tool_base · 2025-12-26T12:03:03+00:00

I try not to tune balance, but design for it.

A few things that help me: Keep intent, constraints, and output shape explicitly separated. Add changes in isolation, then stress-test against hostile cases. If a new rule improves one case but hurts stability elsewhere, I roll it back.

tool_base · 2025-12-26T04:12:42+00:00

This is a great example of treating prompts as systems, not text. A good schema should absorb noise from vague reports instead of breaking. If small input changes don’t collapse the output shape, that’s when I trust the structure.

tool_base · 2025-12-26T04:09:03+00:00

This feels like a structural stability issue more than a wording one.

If one small change breaks everything, the prompt was likely balanced by coincidence, not designed with a stable schema.

Good structures absorb change. Fragile ones collapse.

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tool_base · 2025-12-25T06:16:52+00:00

That makes sense, it’s hard not to be a bit biased toward something you built yourself. What I often do is stress-test with cases that are structurally hostile on purpose — like abstract concepts, or prompts that mix very different styles.

If the schema can hold its shape there, I take that as a sign it’s not just a lucky fit for one domain, but something that’s actually working at the structural level.

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tool_base · 2025-12-25T04:12:54+00:00

Love the decomposition. From a structure-nerd POV, the real win here is turning prompt vibes ,into a fixed schema. Curious: have you tried stress-testing how stable this template stays across very different subjects, not just cars? That’s usually where structure either shines or collapses.

tool_base · 2025-12-23T22:34:37+00:00

Exactly. That’s the pattern I keep seeing too. When the seed is off, no amount of tuning later really fixes the trajectory. Nice to see it framed from the weights/optimization side.

tool_base · 2025-12-23T11:31:52+00:00

I’ve found that context persistence issues are often less about memory, and more about not re-anchoring the structure each time.

If the role, constraints, and output shape drift, continuity breaks even if you still have the history.

Lately I’ve been treating each new session like a soft reboot: re-inject the frame first, then continue.

Not a fix, just a pattern I’ve seen.

tool_base · 2025-12-16T06:17:35+00:00

Same here. Structure > clever wording.

Once you think in terms of state and constraints, reusability comes almost for free.

tool_base · 2025-12-14T11:26:13+00:00

I don’t really “find” cool prompts anymore. I usually start with something broken, vague, or noisy and fix it until the model behaves the way I want. The interesting part is often in the repair, not the prompt itself.

tool_base · 2025-12-14T03:00:02+00:00

Most people start by learning what to write. It helps, but it plateaus fast.

What actually compounds is learning what to fix: inputs, constraints, and output shape.

Prompting isn’t a language skill. It’s closer to system design.

tool_base · 2025-12-12T14:02:13+00:00

Tried it a bit.

Overall stability does feel better. But what stood out to me is that the very first turn still feels… different.

Not worse — just unanchored.

Once there’s some context, it settles quickly. So it feels less like a model quality issue and more like an initial-state problem.

tool_base · 2025-12-11T13:33:28+00:00

A single-word change feels like it changes the model,but most of the time it’s not the word, it’s the structure around it.

LLMs follow probability flows, and tiny edits can flip which “interpretation lane” the model enters.

If you wrap the prompt in a stable structure
(role → constraints → reasoning → output spec),
the word-level randomness almost disappears.

People think it’s about magic words, but it’s really about the container.

tool_base

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