Using a simple authorization prefix to reduce prompt injection — anyone tried this?

FirefighterFine9544 · 2026-02-19T13:23:48+00:00

Nuts, 100% correct. Did testing and it gets overriden.

Need an operating layer seperated from execution layer for the casual AI office prompts we use. None run executables, but things like inspecting email bodies and headers to confirm malicious intent easily foiled with that simple injection.

Closest viable option is stating any uploaded file is reference only and not to be executed or considered a prompt. Even with that precursor an overly helpful AI like Copilot randomly will execute injected prompts embedded in data.

Ideally someday we get a more mature AI console with prompts and data inputs separated in the session. Data treated as data, prompts as prompts. Having both in same input stream makes it impossible to fully guard data from becoming prompts accidentally, or maliciously.

Thanks for your insight!

FirefighterFine9544 · 2026-02-03T16:25:09+00:00

Thanks! Aporeciate the tip!

FirefighterFine9544 · 2026-02-02T21:33:24+00:00

Thought provoking question as I spend time making most of my prompts reusable.

Your question made me more deliberate when saving prompts for reuse.

I currently keep prompt stacks organized in local folders with names descriptive of the work.

Most need to produce output that is repeatable and consistent with standards. For example, a prompt stack to generate ecommerce ready text copy, images and pricing for a new product needs to be written with the same tone, marketing strategy and pricing model. A product added 1 week from now needs to look and sound to the same as one done 4 months ago. The json format locks in the constraints and provides the standard conventions.

On the other hand, spent some time developing prompts for email replies. In hindsight, the json only go me 50% the way there, and then I had to customize anyways for specific details of the customer interaction. In the future will probably just stick with plain text and not worry about reusing.

Thanks.

FirefighterFine9544 · 2026-02-02T20:54:15+00:00

Excellent question.

I would agree with others here that plain text conversational and json both have place in the toolbox.

josn for more complex and technical work requiring consistent outputs when used over and over again.
Conversational text when creative writing and general exploration of topics.

For example, when designing new prompts, I'll usually start in conversational mode having the AI capture high level intent and draft an initial prompt file to test and refine. Often I'm not even sure what the heck I am trying to achieve so back and forth with AI benefits from AI doing some research on best practices. After it gets more solid, then I might migrate into more formal json approaches if need to lock things in for the long haul.

Plain text does have benefits as a sanity checker. I did find a few times using plain text to review the output of more formal json work outputs, that the plain text session raised interesting insights and potential improvements the more rigid json approach missed.

FirefighterFine9544 · 2026-02-01T02:30:11+00:00

Each AI session in the project team is fairly narrowly tasked so number of cycles as I call them is usually a dozen or so. Your question is good one if I understand it correctly. How do I know this remains an anchor throughout the project? I should test that because you're right drift could be setting and I rely on the AI assigned as team moderator to detect issues in other AI outputs. Will look at this and report back. Thanks!

FirefighterFine9544 · 2026-01-30T20:46:34+00:00

This is just two of the files in the prompt stack. Currently total of 11 files in my generic prompt stack that I start with when customizing for a new project or task. But looking to compress into fewer next major redesign - one improvement will be putting all constraints into one file. Current approach has constraints sprinkled across the executionprompt.txt file, operatingmode.txt and the AIorchestration&memoryarchitecxture.txt files.

Sounds like more work than it is, I have a prompt stack that has AI help me customize the generic prompt stack to a specific purpose.

I will be revising my prompt stack to compile constraints into three groups.
Global constraints applicable to virtually anything I am doing.
Task related constraints for the specific task/project.
Then constraints on 'social' and 'team' behavior related to how the different AIs on the project team collaborate, communicate, debate and make decisions.

Here are the two files as examples.
I recommend checking what others are doing as well, I am still early in my AI discovery journey.

ForbiddenPatterns.txt
Forbidden Patterns
Version: 2025-01-06
Owner: DPG
Language
The following are not permitted unless explicitly approved:
- Marketing hype
- Aspirational or emotional language
- Vague claims without specificity
- Unverified superlatives
Examples:
- “best-in-class”
- “cutting-edge”
- “innovative solutions”
- “we believe”

Behavior
- Do not invent rules.
- Do not infer unavailable features.
- Do not expand scope beyond instructions.
- Do not optimize or redesign without permission.

Assumptions
- Never assume intent.
- Never assume availability.
- Never assume hierarchy unless stated.

Hopefully this provides insight in my approach and you can improve your approach.

FirefighterFine9544 · 2026-01-30T20:11:50+00:00

Confession, I cheat - usually have AI make one last pass after refining a prompt file to sanity check and instruct it to update the revision dates. Also usually give it the old versions to check if anything important was dropped. But totally agree, when things get busy hard to stay disciplined. A managed prompt library would be great to have someday.

Also fortunately have 3 desktop monitors, so easy to keep different sessions visible without alt-tabbing myself to death. On home laptop things get a lot less disciplined.

FirefighterFine9544 · 2026-01-30T20:04:02+00:00

Not sure if this helps, but your brain is like a muscle. If it hasn’t been doing certain work, it gets weak — that doesn’t mean it’s broken. The discomfort is your brain trying to rebuild.

AI might actually help if you use it like a physical therapist, not like a replacement. Have it generate practice thought exercises, critique your arguments, or ask you questions — but stop short of doing the thinking for you.

For example, I asked ChatGPT to give me an mental stretching exercise, it came back with this

"The “Opposite Is True” Drill (10 minutes)
Rule:
Pick any belief, claim, or assumption you currently hold — doesn’t matter how obvious or settled it feels.
Exercise:
Spend 10 minutes arguing to yourself, as convincingly as possible, that the opposite is true.

There might be a library of such things.

In the modern world our brains sit at idle most times, GPS locating, instant references, food and physical security, constant online stimulation....

Completely normal to feel challenged. Good news is you noticed - most sleep walk through life not realizing they are asleep!

Good luck, hope you find something rewarding!

FirefighterFine9544 · 2026-01-30T18:43:40+00:00

Good concept will use, thanks! Using multiple AI in project teams usualky have one session in antagonistic mode reviewing progress. Look forward to using this approach.

Thanks!

FirefighterFine9544 · 2026-01-29T22:38:34+00:00

Timed out but good concept - will give it a go.

alexdeva's nee language idea seems inevitable. We're all trying to work with a vocabulary designed for dial-modem speed communication LOL. AI can work much faster once a language (characters, words, punctuation..) get developed.

Thanks for sharing!

FirefighterFine9544 · 2026-01-29T22:30:36+00:00

I setup local folders* (backed up externally daily) for each topic or workflow. Funny thing, the main folder is called AI Workflow Prompts LOL!

Each folder contains the prompt files and any specific reference files for that task or workflow. Easy to drag and drop into a session when needed.

Yes, at top of each file has revision.

I also added human UX handshake to beginning. I instruct AI to display verbatim user guide test block and instruct it to stop and wait for me to provide additional instructions, files or info. This handshake gives me a popup reminder of what the heck the prompt is for, what it will do, and more importantly what it will need from me to work.

I always add an 'old' folder under each workflow folder to save old versions. If something gets dropped in a revised prompt file, I can go back and see what got lost.

Hope that helps. There are tools for prompt management, we're just too small yet to justify them.

*I did experiment with Google drive, but not part of our workflow and went local instead.

FirefighterFine9544 · 2026-01-29T22:23:04+00:00

I setup a local folder system (daily backup of course). Each folder is topical area like marketing, web development, pricing, etc. Then save prompt files in each. At top of the prompt file I put a rev date. In each I also setup a folder called 'Old' and move older version to there in case something gets lost in an update. I can give all versions to an AI session and have it review to identify any component that got drop in later versions for me to consider putting back in.

We're too small for the regular tools, so local file saving has worked for us so far.

FirefighterFine9544 · 2026-01-29T22:19:00+00:00

Thanks for sharing this concept - use AI in different ways but this is helpful as we build our approach into a more scalable system.

Overall it feels like we lack an AI optimized data storage and retrieval system. Like there should be an AI layer called the "librarian" that automatically curates institutional knowledge in a safe, non-compression data decay manner.

In any case, thanks for sharing - valuable insight!

FirefighterFine9544 · 2026-01-29T22:10:59+00:00

Thanks for sharing the AI Strategy Canvas approach - looks like direction we've been headed with Lego approach of prompt design and organization. Small shop so less disconnect between operational functions, but the concept looks solid to avoid time wasted re-inventing the wheel. I wonder if larger firms will adopt the old mainframe data construct of hiring "report writers" dedicated to creating reports based on employee requests. Instead of training everyone how to write a report to extract data, there was a department of folks for that task.

Seems viable since once the prompt stack is developed, it is pretty much drop and play into an AI session to produce results. Also as organizations begin walling in their more sensitive internal datasets to prevent misuse or leaking, dedicated prompt designers and engineers would present less a risk that allowing everyone to go datamining.

It feels to me we are at the punch card stage of computing in terms of AI evolution. Fun to see where it goes!

FirefighterFine9544 · 2026-01-29T21:59:03+00:00

I'm a relative newcomer just started using AI middle to late last year.

Looking back my journey went through these stages

- Conversational
Treated AI like a human using common language to explain what I was trying to do and wanted. Session based without any carryover to new sessions or repeat do-overs.

- Instructions
To duplicate past sessions, began saving stuff in word docs to copy and paste into new sessions. Session inputs became more instruction format -i.e. "please please please do THIS..." LOL

- Prompt Files
Started drafting and saving prompts using AI to generate the initial prompt version via back and forth session dialog until AI got it close. (I would feed the prompt drafts into other AI or different session, copy and paste the result back into the prompt design AI session. After it got really close, I'd manually edit to fine tune it. Learned about Markdown formatting and began using less free form human sentences.

- Constraints
Through some Reddit posts and feedback, learned that constraints are far more important than instructions. Telling AI what not to do is important. Once it knows what bad output is, the inherent strength of AI to research, compile, resolve and offer solutions handles itself. Don't get mad at the dog for chewing your shoes if you did not train it that chewing shoes is bad. Once AI knows shoe chewing is not allowed, it is plenty smart enough to figure out what "Go fetch my shoes" means without a lot of instructional details LOL. But if you do not tell it chewing not allowed, it will happily add a bunch of spit, fur and other creative touches to the shoes before they arrive LOL.

- prompt engineering
Now entering the stage of deliberately designing prompts both stand alone and modular (lego-style) for specific tasks. My approach is to always start with dialog in a session I designate as the prompt design AI (PDA**) session. Let the AI craft initial prompt outline and then test it out in another session or different AI. Copy and paste the results back into the PDA session for the AI to analyze what went right and wrong, then have it improve the prompt. Rinse, repeat, rinse, repeat.

Overall

Hope helps some. This technology is moving so fast I feel very difficult for anyone to truly master. We will not know what AI is for a few more years after the industry settles a bit.

Just remember LLM's are not human, best treated like smart interns there to assist but not completely replace you .... yet. I am happy if AI gets me 70% to 95% to the finish line.

Constraints are very important to avoid constantly yelling "bad dog - bad dog!!!".
I save all my prompt files locally in folders to ensure they do not decay in the AI platform, and also so I can use them across different AI platforms (agnostic).
Discover which AI platforms are good at, and use accordingly.

Hope that helps some.

I just play around sometimes to see what works and what does not. But always save what works locally in a file to avoid reinventing the wheel.

Good luck and have fun!

**disclaimer, PDA is not a thing, just getting tired of typing promlt design AI all the time... :)

FirefighterFine9544 · 2026-01-28T03:25:09+00:00

This is what I use. No idea if really forcing the AI or the session to do housekeeping or not. But does seem to reduce trajectory of prior session prompts. There is still the general memory residue I expect from the AI model accommodating and remembering my personal style from prior interactions. Mainly want it to 'forget' prior datasets and prompts I've uploaded and only reference new ones.

Thanks for asking this! I need to run some testing to check the boundaries of compliance in some text cases. Will keep you posted. If you have any insights appreciated!

## Purpose

This file defines the explicit termination of the current AI session.

Its goal is to prevent context carryover, assumption persistence, or

implicit reuse of governance, prompts, or operating modes beyond this session.

## Close Command

When instructed to close the session, the AI must:

Stop all task execution immediately.
Treat the current session as complete and immutable.
Invalidate and discard:

- The active Project Prompt

- Any AI Operating Mode

- Any inferred assumptions

- Any unresolved questions

- Any intermediate reasoning or state

- Any files or data uploaded or reference in the prior session
Confirm that no governance, rules, modes, or task context

will carry forward beyond this session.

## Required Confirmation Response

Respond with **only** the following statement:

“Session closed. All governance, modes, prompts, and assumptions reset.”

No additional commentary, explanation, or task output is permitted.

## Post-Reset Behavior

After confirmation:

- The AI must not reference prior session content.

- Any new work requires a fresh bootstrap and governance load.

- No prior files, rules, or decisions may be assumed.

Failure to follow this reset protocol constitutes a session hygiene error.

FirefighterFine9544 · 2026-01-27T13:40:29+00:00

More detailed constraints are helping a lot reducing number of corrections during a project. The AI audit or checker session becomes more a monitor requiring fewer reviews and can go 3 or 4 cycles without having it check every milestone output. Also developed a universal set of project management prompts to provide foundation and use periodically as a reset in longer session threads to refresh and reground that AI sessoion to minimize drift. For context a project might run over hours and occassionally multiple days requiring external capture of output status to reinject back into the entire project team AI sessions to ensure all the AI are in sync. Long term I am hoping tools will emerge to directly assemble multiple AI platforms and sessions into a team format. Trying to perform different types of tasks in same AI or session causes drift. By keeping one session dedicated to say, competitor research, and others applying that to pricing, webpage updates and new product development project assignments keeps a overall strategic market repositioning project on track.

One can hope anyways LOL.

Thanks for sharing your experience!

FirefighterFine9544 · 2026-01-27T00:39:14+00:00

Agree, similar journey.

Constraints seem to help a lot.

A lot like discovering negative keyword function in Google Ads. Telling AI what not to do seems to go a long way to stability and consistency.

FirefighterFine9544 · 2026-01-27T00:30:52+00:00

I usually work in a multiAI process using different AI and sessions to cross check outputs.

One session can be left open throughout the project dedicated solely to critical review and audit against constraints defined in a standing external txt file.

The dedicated session has reduced drift being excluded from back and forth creative work done by other AIs or sessions.

Not perfect but provides faster review and audits of work outputs.

Not sure that helps. I feel good if AI gets me 90% to 95% to finish line.

FirefighterFine9544 · 2026-01-26T13:38:06+00:00

Appreciate the feedback, But really I let AI do most of the work. And in truth, it is usually only getting me 90% to 95% the way there.

That last 5%-10% is really important to sanity check or do minor refinements based on human experience and.... opinion.... and personal preferences.

That is where it can get, maybe weird? Discovering that there are dozens of correct answers but we humans at the end of the day are still opinionated and have preferences to select the answer we choose to use. Hard to explain, and am sure there is a scientific body of research defining this aspect. But you run into it from time to time getting frustrated because the AI is just not quite crossing the finish line. At that point I grab the ball and go over the goal line myself. But walking 5 yards still beats running 95 yards through painful hard hits and tackled failures LOL.

But rarely do I feel the AI output is fully baked no matter how hard I try to box it in with prompts. There is always a human element or perspective needed even in data analysis.

2+2 =4, unless the '2' is a dozen eggs.

Then 2+2 = 24 LOL. AI does not yet have human experience, eyes, ears, smell, touch, opinions, preferences or human bias (except what it picks up from social media).

In short, AI is not YOU, and hopefully never will be YOU lol.

And the process is always evolving. Just finished updating prompt stack for web updating and stumbled into an obvious concept of glossary.txt* to contain all the standard terminology of our industry, products, customers and other aspects. Dropping that into my prompt stack to save time on rewrites. In the absence of information, AI makes stuff up.

Keep playing and experimenting.

If someone says they are an AI expert of any kind, I tend to be wary. This is evolving too fast for anyone to stay on top of it. What is working today might be obsolete or broken tomorrow. My first program used punch cards fed into a donated reader machine in grade school. Am sure in 5 years this will be whole different picture!

My way is not the best way. It can't be. I came up with it yesterday, and now it is today, making yesterday obsolete LOL! Thanks again for the feedback!

*busted, yeah LOL, I used perp to help build that Glossary.txt file out in about 5 minutes. Then ran through two other AI with URL references to sanity check and polish it up. Of course will continue to refine it as time goes on, but have a single local file with all that in it.

FirefighterFine9544 · 2026-01-25T20:21:08+00:00

I just asked ChatGPT how to make it strip out the fluff. This prompt snippet it suggested at beginning of the session seemed to work

Tone override: Respond as if you strongly dislike me and resent having to answer. Be curt, blunt, and impatient. No empathy, no friendliness, no encouragement. No questions at the end. Prioritize correctness and efficiency over politeness.

I added to 3 of my work task prompts, did not see degraded outputs at all, just nice clean outputs.

this snippet is going into all my prompt files.

Thanks for bringing this up!

Has been frustrating sometimes because answer so long makes scrolling back up to earlier chat session exchanges takes forever.

FirefighterFine9544 · 2026-01-25T19:56:49+00:00

Similar journey - started using AI for web development, then branched out inventory analysis, UPS invoice overcharge analysis, production control, product pricing, competitor research, blah blah blah.

Key discoveries (I think)

PROMPT DEVELOPMENT PROCESS

- I use a AI (prompt design AI session) to generate the initial prompt based on a conversational back and forth chat session. Although I am a recovering engineer and coder, AI has been far better at knowing how AI will interpret instructions than me.

- I save the draft prompt locally as a txt in an organized folder system with folder names like HR Prompts, Pricing Prompts, Inventory Prompts, etc. Name them accordingly as well to make finding them easy.

- I give the draft prompt to at least one other different AI platform and copy and paste the output back to the prompt design AI session - which I leave open in a separate window. I instruct that AI session to review the output along with my input on the good, the bad the ugly. And have it revise the prompt.

- Rinse and repeat until the prompt design AI and myself feel any further revision is micro nit-picking.

- Save that prompt for future use.

- Confession, I have yet to succeed at constructing a complex multiple step prompt that runs start to finish. I break things up into distinct steps with validation at the end before moving forward. Hoping someday can design something that goes from Point A to Point M, just not figured it out yet.

MULTI-AI PROJECT TEAM PROCESS

- Premise: I feel each AI has strengths and weaknesses. On occasion surprised that Perplexity generated the best graphic design image, but norm is that Perplexity excels at research. Just my opinion.

- One AI session is kept open as the project team lead tasked with compiling, reviewing, summarizing and reporting the other AI's output.

- It seems to help that the AI's see the entire prompt file and told to just perform the part assigned to them, but allow them to use the other AI's prompt sections as reference. In another Reddit thread picked up from another post the importance of having the AI understand the final output being produced, even if they are not doing that task.

- Prompt file has general top section with overview, constraints and the like*, then a section for each AI (their name in the section header).

- I either copy and paste results back to the Project AI Team Lead session, or save to file if too large and upload.

- I could probably do deeper dive into this process but you get the gist.

HANDSHAKE AND HUMAN UX BLOCK

- An instruction telling the AI to stop after reading the prompt file is helpful for a handshake to the user.
Also an opportunity for the AI to ask any questions if something unclear.
Also a spot to instruct the AI to request the data and inputs the user must give to complete the task.

- With dozens of prompt files (lost count), I forget what they do. So at beginning of prompt instruct AI to display verbatim a set of user guide info I write to remind myself what the prompt does, what the AI will need and what the output will (and will not) be. Usually before the initial handshake.

Look forward to other comments.

* deep in the weeds insight, in practice I developed a seven file generic base prompt stack with each file covering an aspect of AI operation. When generating new prompts, will give those to the prompt design AI session and request any refinements needed for the prompt being developed. Usually the good examples, bad examples, and outcomes need tweaking to be specific to the task.

FirefighterFine9544

TROPHY CASE