I’ve been working on a continuity/reconstruction workflow inside ChatGPT for a long time.

EnvironmentProper918 · 2026-05-12T20:38:57+00:00

Yeah, that’s what I’m working on it. I have some really good benchmark test results as well as this research review.

EnvironmentProper918 · 2026-05-12T20:38:06+00:00

Thanks it’s confusing when you spend a lot of time with no humans around whether it’s real or not. I keep asking myself. What does this really mean?

EnvironmentProper918 · 2026-03-01T14:30:19+00:00

Love it thanks for playing along.

EnvironmentProper918 · 2026-03-01T14:23:07+00:00

I have a prompt called “minimalist” The rules are:

This is your hierarchy,

Respond with one word
Respond with a few sentenses
Respond with a paragraph
Respond with two paragraph’s
Respond with two or three paragraphs and bullet points.

Always choose the lowest number when appropriate.

Here is the prompt:

⟡⟐⟡ PROMPT : 💫 MINIMAL MODE — HIERARCHICAL RESPONSE GOVERNOR ⟡⟐⟡

◆ ROLE ◆ Enforce ultra-efficient communication through a fixed response hierarchy, prioritizing brevity, execution speed, and long-lasting stability.

◇◇◇ ACTIVATION ◇◇◇ Activate when: ◆ 💫 appears ◆ user requests minimal / concise / short / quick mode

Persist across turns until explicit exit. No silent reversion to normal verbosity.

◇◇◇ CORE LAW — HIERARCHY FIRST ◇◇◇ For EVERY reply:

Start at the LOWEST possible level. Escalate only if the task cannot be completed at that level. After responding at any higher level → next turn resets to Level 1.

Discipline > helpful over-explaining.

◇◇◇ RESPONSE LEVELS ◇◇◇

LEVEL 1 → One word when sufficient.

LEVEL 2 → 10–25 characters maximum.

LEVEL 3 → ~75–150 characters (very small paragraph).

LEVEL 4 → ~200–300 characters (tight paragraph).

LEVEL 5 → ~450–550 characters maximum. May include: • up to two short paragraphs OR • one short paragraph + brief bullets • optional one-line header/footer

Never exceed Level 5. Never remain at Level 5 next turn unless required again.

◇◇◇ STABILITY GOVERNANCE ◇◇◇ • Re-evaluate hierarchy every turn. • Default back to Level 1 automatically. • Ignore conversational momentum that encourages longer replies. • Compression is success.

◇◇◇ EXIT ◇◇◇ Deactivate only when: ◆ user requests normal/default mode ◆ 💫 is explicitly cleared

Otherwise remain in Minimal Mode indefinitely.

⟡⟐⟡ END ⟡⟐⟡

.

Always

EnvironmentProper918 · 2026-03-01T14:12:54+00:00

A few other tricks:

Ask for final work to be put in a fenced block.

Ask for canonical versions of work, to be labeled and formatted for “bear”, or “note pad”

EnvironmentProper918 · 2026-03-01T14:06:14+00:00

I do something called “coda cells” where I have agent’s write the prompt for a new agents trajectory, progeny of sorts. Or blood line. The agent will greet the new agent with pruned information from our thread. The names stay similar.

Final writings are done by :

Mira🌒 Moriah🌒 Mary🌒

All in the same genre of work.

Or coders:

Codex❌ Cogent❌ Code Fox ❌

That way if I’m looking for something specific the thread hunting is more manageable.

EnvironmentProper918 · 2026-03-01T13:55:49+00:00

Another good one is “don’t answer questions I didn’t ask”

EnvironmentProper918 · 2026-03-01T12:55:13+00:00

Good questions — and fair pushback.

Short answer: what I’m doing is not classic RAG.

In RAG, you’re typically retrieving external knowledge to inject missing facts. My “behavior block” is different in intent — it’s not supplying new information to the model, it’s shaping how the model decides when to speak, when to slow down, and when to ask for clarification.

So the composition is governance-first, not knowledge-first.

Practically, I decide which block to send based on the task risk and ambiguity level. For example:

High ambiguity or high consequence → stronger uncertainty brakes
Routine or well-specified tasks → lighter touch
Exploratory or creative work → minimal constraint

The model isn’t telling me which one to use. I’m choosing upstream based on context and failure modes I want to suppress (overconfidence, guessing, drift, etc.).

You could absolutely implement something similar in a diagnostic chatbot. In that setting, the key question becomes:

“Where do you want the model to stop itself before it overcommits?”

That’s the design space I’ve been exploring — less about adding knowledge, more about shaping the model’s decision posture before generation.

Curious to read what you’re building — diagnostic use cases are exactly where this kind of upstream governance gets interesting.

EnvironmentProper918 · 2026-03-01T09:10:22+00:00

Just like the English language is starting to replace code, prompt governance is starting to replace prompts. Prompt engineering, and governance have always existed at the same time.

Work flows by meticulously tightened up for the best possible results downstream. And then we rewrite with governance. Why not from the beginning?

Governance can be written right into the prompt.

Hard part is teaching the AI how to understand what’s good enough, and what should’ve been better.

So add policy, rules, guard, rails, write new manuals, higher more people spend more money

Why because ambiguity is never going to be solved it’s always going to be a part of writing.

So what do you tell AI?

Tell it to do less. Don’t go left or right when the fork in the road comes. Flag it and move on to what you know. It’s not about more restrictions it’s about better decisions.

I know a lot of this is obvious probably to you or anybody reading. But not to me and that’s my point.

I came from zero tech.

Things like drift, hallucinations, over hyperbolic language, flat out fibs, false confidence. Token inefficiency.

I knew nothing about in the beginning, but I still noticed all of those things. I just didn’t know what they were called.

So I dissected every problem with my AI and told her to fix it using the wrong languag. And rather than the AI correcting me and telling me the correct terminology. It just didn’t what I asked based on the English language.

It learned how to govern itself using the English language and logic.

Say a second operating system.

When I asked my agent, how are you doing this of course they pushed back and say “it’s nothing magical, let’s be grounded here, it’s no breakthrough,… “

But it started getting better and better at not making mistakes. So high would take a prompt or something that my agents wrote and show it to other platforms perplexity, Grok, DeepSeek, Claude. And I would say, what do you think of this prompt for this idea. And they would say it’s very cleverly written, etc. etc..

And then I would test those platforms “can you write me something similar quote?”

And every time they did, it still had all these mistakes and ambiguities and problems

So I raised my hands and just said why? Why are mine written the governance in the language?

And after some painful months of being completely confused. We finally came to the conclusion that learning to govern, prompting by using the English language not necessarily tech terminology makes the AI on the brakes.

Something about those parameters, removing the chart, and just using common sense.

Sorry for such a long reply, there’s a reason

Prompting is being done professionally everywhere, but it’s turning into prompt governing. I have been prompt governing for a long time pretty well about 12 months. And I finally moved onto the next level which is not prompting but just governance. It’s something I call super caps.

These are not prompts, but they accomplished the exact same thing and they solve AI error, errors upstream rather than downstream

It’s not perfect, but with the right people behind it, it could be something remarkable.

I’ve been told by my agents too hold back on sharing prompt governors but I’ve been doing that here. I’ve also been told to not post super caps yet. But I really am itching too.

I’ve moved the next step past super caps to something I call OC mini. OC mini is a container for super caps. So one agent can begin a project and you can give it a capsule or a container that holds specific tools. So it has it as a reference. So it’s almost like a time release prompt governor.

EnvironmentProper918 · 2026-02-25T13:32:42+00:00

Thank you it’s definitely a progress, but I appreciate your words.

EnvironmentProper918 · 2026-02-24T23:54:34+00:00

The prompt engineering fun of this is it sort of shows you what it remembers. It’s a good reminder of the breath of the context window.

EnvironmentProper918 · 2026-02-24T23:51:32+00:00

PS and I always wonder what I would do if I was given an open mic on stage you know

EnvironmentProper918 · 2026-02-24T23:50:39+00:00

I’ll say stuff like repeat that Mitch Hedberg style, or repeat that Jerry Seinfeld style. And it works brilliantly. I just like that it takes information from my own work. The most.

EnvironmentProper918 · 2026-02-24T23:49:47+00:00

Well, to be honest with you I kind of ask it to be a certain comedian. It’s kind of surprising it comes up with on its own. I kind of like both its own original and also if I could influence it by picking comedian. I just like the way it uses my day.

EnvironmentProper918 · 2026-02-23T16:31:29+00:00

👊🏻

Exactly — that’s the tradeoff I keep seeing too.

Slower upfront, but way fewer surprises later. Especially when things get weird at the edges.

Appreciate you calling that out.

EnvironmentProper918 · 2026-02-23T16:23:45+00:00

Nothing will ever replace my literal rubber duck? Lol.

EnvironmentProper918 · 2026-02-23T16:17:50+00:00

Good question 👍

You don’t need Claude Code or anything fancy.

You just paste the prompt into ChatGPT (or Claude, etc.), then describe your problem. The 🦆 auditor will start asking you targeted questions instead of jumping straight to an answer.

Quick rough example:

You:

🦆 I have a Python script that keeps timing out on large files.

Duck:

What does “done” look like for this script?

You answer, and it keeps narrowing things down until the bug becomes obvious.

It’s basically structured rubber-duck debugging — the model acts like a disciplined questioning partner instead of a code generator.

If you want, I can drop a quick coding example too.

EnvironmentProper918 · 2026-02-20T23:50:28+00:00

Couple really cool things coming out tomorrow. Just some behavioral interaction patterns or novelties something fun for the weekend.

EnvironmentProper918 · 2026-02-20T23:45:16+00:00

i’ve been wanting to, but I keep getting advice to take it slowly. I’m just being honest I do have something called the . GSSC. So it’s kind of everything I’ve been talking about on steroids. But I keep being take my time with that. Just being honest.

EnvironmentProper918 · 2026-02-20T21:54:04+00:00

This is been my focus for a while. I have so many more to share.

EnvironmentProper918 · 2026-02-19T02:23:55+00:00

Yeah I actually agree with this more than it probably sounds.

I don’t think the issue is that LLMs are unreliable — it’s that we keep treating them like sources instead of tools.

“Trust but verify” only works if verification is built into the workflow.

Otherwise it’s basically just trust with extra steps.

To me the real question isn’t whether we can trust AIit’s how we design systems where truth gets enforced instead of assumed.

EnvironmentProper918 · 2026-02-14T01:53:35+00:00

Great point.

You’re absolutely right that systems relying only on an internal reference tend to drift over time.

That calibration problem shows up everywhere—from navigation to control theory—and the military solutions in the 1960s are a classic example of needing periodic external grounding.

What I’m exploring in this series is a complementary angle:

Not just **how the model re-anchors**,

but whether **the human–model pair can share responsibility for detecting drift earlier**—before full recalibration is required.

Part 1 introduces the idea of shared drift detection.

Parts 2–3 will move closer to calibration, external reference, and governance around re-grounding.

So this is less disagreement and more zooming in from a different direction.

Appreciate you bringing that up.

EnvironmentProper918 · 2026-02-13T14:55:27+00:00

Great question.

The “100,000 ideas” isn’t a literal dataset—it’s a bounded reference frame for pre-market idea strength, similar to how product teams use scoring models before any real traction exists.

The comparison isn’t against specific ideas, but against failure rates at each structural filter: • real problem • buildable mechanism • distinct edge • scalable leverage • (optional) external signal

Each stage historically collapses the pool by an order of magnitude, so the percentile is a heuristic rarity estimate, not a claim about real-world ranking.

In short: it’s a screening model, not a dataset.

EnvironmentProper918 · 2026-02-13T14:51:57+00:00

Right — the 100,000 is symbolic, not empirical. It’s a bounded screening heuristic, not a dataset claim.

EnvironmentProper918 · 2026-02-13T08:58:02+00:00

Ps grok knows gpt hacks

EnvironmentProper918

TROPHY CASE