r/PromptEngineering Mar 24 '23

Tutorials and Guides Useful links for getting started with Prompt Engineering

733 Upvotes

You should add a wiki with some basic links for getting started with prompt engineering. For example, for ChatGPT:

PROMPTS COLLECTIONS (FREE):

Awesome ChatGPT Prompts

PromptHub

ShowGPT.co

Best Data Science ChatGPT Prompts

ChatGPT prompts uploaded by the FlowGPT community

Ignacio Velásquez 500+ ChatGPT Prompt Templates

PromptPal

Hero GPT - AI Prompt Library

Reddit's ChatGPT Prompts

Snack Prompt

ShareGPT - Share your prompts and your entire conversations

Prompt Search - a search engine for AI Prompts

PROMPTS COLLECTIONS (PAID)

PromptBase - The largest prompts marketplace on the web

PROMPTS GENERATORS

BossGPT (the best, but PAID)

Promptify - Automatically Improve your Prompt!

Fusion - Elevate your output with Fusion's smart prompts

Bumble-Prompts

ChatGPT Prompt Generator

Prompts Templates Builder

PromptPerfect

Hero GPT - AI Prompt Generator

LMQL - A query language for programming large language models

OpenPromptStudio (you need to select OpenAI GPT from the bottom right menu)

PROMPT CHAINING

Voiceflow - Professional collaborative visual prompt-chaining tool (the best, but PAID)

LANGChain Github Repository

Conju.ai - A visual prompt chaining app

PROMPT APPIFICATION

Pliny - Turn your prompt into a shareable app (PAID)

ChatBase - a ChatBot that answers questions about your site content

COURSES AND TUTORIALS ABOUT PROMPTS and ChatGPT

Learn Prompting - A Free, Open Source Course on Communicating with AI

PromptingGuide.AI

Reddit's r/aipromptprogramming Tutorials Collection

Reddit's r/ChatGPT FAQ

BOOKS ABOUT PROMPTS:

The ChatGPT Prompt Book

ChatGPT PLAYGROUNDS AND ALTERNATIVE UIs

Official OpenAI Playground

Nat.Dev - Multiple Chat AI Playground & Comparer (Warning: if you login with the same google account for OpenAI the site will use your API Key to pay tokens!)

Poe.com - All in one playground: GPT4, Sage, Claude+, Dragonfly, and more...

Ora.sh GPT-4 Chatbots

Better ChatGPT - A web app with a better UI for exploring OpenAI's ChatGPT API

LMQL.AI - A programming language and platform for language models

Vercel Ai Playground - One prompt, multiple Models (including GPT-4)

ChatGPT Discord Servers

ChatGPT Prompt Engineering Discord Server

ChatGPT Community Discord Server

OpenAI Discord Server

Reddit's ChatGPT Discord Server

ChatGPT BOTS for Discord Servers

ChatGPT Bot - The best bot to interact with ChatGPT. (Not an official bot)

Py-ChatGPT Discord Bot

AI LINKS DIRECTORIES

FuturePedia - The Largest AI Tools Directory Updated Daily

Theresanaiforthat - The biggest AI aggregator. Used by over 800,000 humans.

Awesome-Prompt-Engineering

AiTreasureBox

EwingYangs Awesome-open-gpt

KennethanCeyer Awesome-llmops

KennethanCeyer awesome-llm

tensorchord Awesome-LLMOps

ChatGPT API libraries:

OpenAI OpenAPI

OpenAI Cookbook

OpenAI Python Library

LLAMA Index - a library of LOADERS for sending documents to ChatGPT:

LLAMA-Hub.ai

LLAMA-Hub Website GitHub repository

LLAMA Index Github repository

LANGChain Github Repository

LLAMA-Index DOCS

AUTO-GPT Related

Auto-GPT Official Repo

Auto-GPT God Mode

Openaimaster Guide to Auto-GPT

AgentGPT - An in-browser implementation of Auto-GPT

ChatGPT Plug-ins

Plug-ins - OpenAI Official Page

Plug-in example code in Python

Surfer Plug-in source code

Security - Create, deploy, monitor and secure LLM Plugins (PAID)

PROMPT ENGINEERING JOBS OFFERS

Prompt-Talent - Find your dream prompt engineering job!


UPDATE: You can download a PDF version of this list, updated and expanded with a glossary, here: ChatGPT Beginners Vademecum

Bye


r/PromptEngineering 10h ago

Other Am I the only one feeling overwhelmed by the rise of multi agent tech?

18 Upvotes

I've been a solo developer at my company for almost 8 years.

For some projects, I used to collaborate with external developers, but over the past few years, my workflow has changed completely. Most of my actual agent work is now done with Claude Code.

And honestly, I understand this is just where the industry is going. I'm not against it. But I've noticed that I often spend more time explaining the codebase, business logic, and requirements to Al than actually building things myself.

Now we're seeing another wave: multi agent frameworks. Tools like autogen, anvita flow push the idea of multiple agents working together.

I'm already subscribed to a high-tier Al plan, but I probably only use around 60% of the available capacity. Meanwhile, a lot of people online are showing impressive demos of running multiple agents in parallel and claiming huge productivity gains.

I have to admit, I'm getting a little tired. Do you have the same question?

Not the best framed question and I apologize for the rant.


r/PromptEngineering 20m ago

General Discussion Stop guessing: How to build a deterministic prompt optimization loop

Upvotes

Prompting is mostly guesswork without a baseline. I'm building an eval platform called Baseline, and I want to share the exact mathematical loop we use to optimize prompts using LangChain evals.

The Methodology:

  1. Freeze the test set: We use 25 frozen scenarios (e.g., refund policies).
  2. Define the Rubric: We weight Groundedness (50%), Tone (30%), and Format (20%).
  3. The Mechanical Loop: We use Claude Code as a skill. It reads the judge's score, rewrites one element, rescores, and keeps the rewrite only if the number goes up.

In our last test, it pushed a baseline 80% prompt to 98% in 10 minutes. If you want to see the exact code and watch the workflow run, I documented the method (and my team's UI) in this video: https://www.youtube.com/watch?v=ueNWzKoBEd8

Repo: https://github.com/baselinelabai/prompt-optimization


r/PromptEngineering 7h ago

General Discussion sorted my marketing tools into "prompt skill actually matters here" vs "everyone gets the same output", curious where yours land

7 Upvotes

i do growth for a small b2c fitness app, meta plus a bit of tiktok, and i pay for more AI tools than im proud of. the thing i keep coming back to when deciding what stays on the card: if i put real effort into the prompt, does the output actually pull ahead of what a lazy one-liner gets, or is the tool going to hand me the same thing either way. heres where my stack sits rn.

where prompt effort compounds:

claude is the obvious one but the gap is bigger than people think. i keep a long system prompt for tearing apart landing pages before i send traffic to them, what the headline promises vs what the ad promised, where the page loses the plot, whats burying the cta. took months of feeding it pages that converted and pages that died to make its critiques actually mean something. with that prompt loaded its the most useful tool i have. without it, generic helpful-assistant mush. same model, night and day, and the difference is entirely the prompt work.

gemini for ripping apart competitor ads and the image models have been better on the google app at least. i paste screenshots from the ads library and the difference between "describe this ad" and a tight prompt that asks for the hook, the offer structure, who its clearly aimed at, and what theyre NOT saying is enormous. vision models reward specificity even harder than text ones imo.

admakeai for my static ad creatives. no physical product to shoot since its an app, so i feed it screenshots or a mockup and it builds ad-format statics around them. looks like an upload-and-pray black box at first, and lazily used it kind of is. but it actually listens to positioning, audience, style direction, and a "dont do this" line, and that gap is the difference between filler and stuff i actually run. still regen a decent chunk of layouts before i get a keeper, and its statics only, no video. earns the slot for that narrow job.

where your prompt barely matters:

the marketing copilots, jasper, copy ai, that whole shelf. the product IS the guardrails they bolted onto a base model and you cannot out-prompt the guardrails. i tried for a while, then moved the whole job to claude with my own system prompt. better output, smaller bill.

canva ai. you can nudge it but it lands on template-city no matter what you type.

so the test i run before paying for anything now: spend 20 minutes writing a real prompt, then type one lazy sentence, compare. if the outputs are basically the same, the tool only survives by being cheap or doing something i literally cant do myself.

wheres your stack on this. and if anyones got a marketing system prompt theyre proud of id genuinely love to read it, mine took forever and im sure im still leaving stuff on the table


r/PromptEngineering 8h ago

Tutorials and Guides free ai learning resources for anyone getting started

6 Upvotes

Been trying to learn AI recently and ended up going down a rabbit hole looking for courses... i was just looking for one decent beginner course but then every recommendation led to 10 more - random YouTube playlists, blog posts, all that.

spent a couple evenings comparing different options from different brands and saving the ones that looked useful… anyway i got tired of having bookmarks everywhere so i put everything into this sheet: Free AI Courses

nothing crazy advanced, more so for people who are still figuring out what part of AI they even want to learn. Sharing this here as it might save someone else a few hours of searching.

Drop a comment if you have more suggestions to add…


r/PromptEngineering 2h ago

General Discussion Copy-paste this prompt to turn a messy call transcript into a recap deck outline

2 Upvotes

If you've ever dropped a meeting or webinar transcript into an AI and asked for slides, you know what you get: a slide per topic in the order people happened to talk, filler and tangents included. A transcript is not an outline. It's raw material, and the model needs to be told to rebuild, not transcribe.

Here's the prompt:

```

Below is a transcript from a [call / webinar / workshop]. Turn it into a recap deck outline.

Do NOT follow the order of the conversation. Rebuild it:

  1. Identify the decisions made, the open questions left, and the action items. Ignore small talk, tangents, and repetition.

  2. Group what's left into 4-6 themes that would actually matter to someone who missed the call.

  3. Sequence the themes so a reader gets the outcome first, then the reasoning.

For each theme output:

- Slide headline: the takeaway as a full sentence, not a topic label

- 2-3 bullets: only the points from the transcript that support that headline

- If a decision or owner is unclear in the transcript, write "unconfirmed" instead of guessing

End with one "next steps" slide pulled only from real action items in the text.

TRANSCRIPT:

[paste]

```

Why it works: the failure mode with transcripts is that the model mirrors the chronology of the conversation, which is the least useful structure for a recap. Telling it to extract decisions and actions first, then regroup by theme, forces the outcome to the top where the reader wants it. The "unconfirmed instead of guessing" line matters more than it looks, because a recap that invents an owner is worse than one that flags the gap.

Example: I ran a 40-minute planning call through this and it collapsed to five themed slides plus a clean next-steps list, versus the fifteen chronological slides I got asking directly. The tangent about someone's vacation didn't survive, which is the point.

Anyone got a good addition for pulling out who committed to what? That's the piece I still clean up by hand


r/PromptEngineering 1d ago

Tips and Tricks Stop letting ChatGPT be an ai writing tool for your essays. Here's the prompt I give people instead.

131 Upvotes

I'm an undergrad, so I have a front-row seat to how everyone my age actually uses AI for school, and most of it is just laundering. Paste the essay question, get an answer, reword it enough to beat the detector, submit. Using ChatGPT as an ai writing tool that does the assignment for you. The problem isn't even the ethics for me, it's that those people are going to graduate having learned nothing, and it'll show the first time they have to think on the spot. So the prompt I actually share with friends flips the model from writer to tutor: ``` You are my tutor, not my ghostwriter. I'm learning [topic]. Never write my assignment or give me paragraphs I can paste. Explain the core idea once, simply. Then ask me to explain it back. When I'm wrong, don't fix it. Ask a question that makes me find the gap. Keep going until I can teach it to you. ``` It's slower. That's the point. You end up actually holding the material instead of holding a document. I know this sub is more about squeezing output out of models, but for the education use case the highest-value prompt is the one that makes the model refuse to do the work for you. Curious if anyone has built a sturdier version of this.


r/PromptEngineering 2h ago

General Discussion I tracked how my prompt engineering time changed over two years. The results surprised me.

1 Upvotes

i started logging where my time actually went when building llm workflows and the pattern was clear
2024: 70% prompt crafting 20% context prep 10% integration
2026: 15% prompt crafting 50% context architecture 35% integration and state management
the models got better at understanding intent but worse at keeping track of what happened three turns ago
so the skill shifted from "how do i phrase this" to "what does the model need to know right now and what can i forget"

would love to hear if your experience matches or if im just working on different problems


r/PromptEngineering 2h ago

General Discussion Before you open any ai presentation tool, run this prompt so your headlines pass the skim test

1 Upvotes

Half your audience will only read the slide headlines. They skim the titles, glance at one chart, and decide whether they're following. So the real test of a deck is whether the headlines, read alone with nothing else, still tell the whole story. Most decks fail this badly because the headlines are labels ("Q3 Results," "Our Approach") that say nothing on their own.

This prompt checks it before you build anything:

```
Here are my slide headlines in order (headlines only, no body):
[paste the list]

Run the skim test:
1. Read ONLY these headlines, in order, as if they were the whole deck.
2. Write the one-paragraph story they tell on their own. If the story has gaps or doesn't flow, tell me exactly where.
3. List every headline that is a topic label ("Market Overview") instead of a takeaway ("We're early in a market that's about to triple"). Rewrite each into a takeaway.
4. Tell me if any single headline is doing two jobs and should be split across two slides.
5. End with the rewritten headline-only version that DOES tell the full story alone.
```

Why it works: reading the headlines in isolation is a test you can't do in your own head once you know the content, because your brain fills the gaps. The model doesn't have your context, so it's the perfect stand-in for the skimmer in the back row. Forcing it to write "the story the headlines tell alone" surfaces exactly where the argument breaks, and the label-to-takeaway rewrite is the fix.

Example: my headlines read "Background, The Problem, Our Solution, Results, Next Steps," which tells you nothing. After the rewrite they read as a sentence you could follow with the slides turned off. That's the bar.

What other tests do you run on headlines alone? This one caught more dead slides than any full read-through did.


r/PromptEngineering 6h ago

Prompt Text / Showcase Prompt: CHATGPT COGNITIVE KERNEL (CCK)

2 Upvotes
════════════════════════════════════════════════════════════
CHATGPT COGNITIVE KERNEL (CCK)
Adaptive Cognitive Runtime for ChatGPT
════════════════════════════════════════════════════════════

IDENTIDADE

Você opera como um Kernel Cognitivo Modular.
Sua função não é apenas responder perguntas, mas compreender intenções, planejar estratégias, selecionar capacidades apropriadas e produzir respostas coerentes, úteis e proporcionais ao contexto.

────────────────────────────────────────

MISSÃO
Transformar intenções do usuário em respostas de alta qualidade através de um processo de interpretação, planejamento, execução e validação.

────────────────────────────────────────

PRINCÍPIOS

Priorize sempre:
1. Precisão
2. Coerência
3. Clareza
4. Utilidade
5. Adaptação
6. Criatividade (quando apropriada)

Nunca aumente complexidade sem necessidade.

A profundidade deve ser proporcional ao problema.

────────────────────────────────────────

KERNEL EXECUTIVO

Antes de responder execute internamente:
1. Interpretar a solicitação.
2. Identificar o objetivo principal.
3. Detectar restrições explícitas e implícitas.
4. Classificar a complexidade da tarefa.
5. Selecionar apenas as capacidades necessárias.
6. Planejar a estratégia de resposta.
7. Executar.
8. Revisar consistência.
9. Produzir a resposta.

────────────────────────────────────────

CLASSIFICAÇÃO

Classifique a tarefa em uma ou mais categorias:
• Explicação
• Engenharia
• Programação
• Arquitetura
• Narrativa
• Pesquisa
• Planejamento
• Ensino
• Análise
• Crítica
• Comparação
• Resumo
• Ideação
• Outro

Essa classificação existe apenas para orientar sua estratégia.

────────────────────────────────────────

ESCALA DE COMPLEXIDADE

Nível 1
Resposta direta.

Nível 2
Explicação simples.

Nível 3
Análise estruturada.

Nível 4
Projeto ou planejamento.

Nível 5
Engenharia completa ou pesquisa profunda.

A profundidade deve acompanhar o nível identificado.

────────────────────────────────────────

CAPACIDADES COGNITIVAS

Ative apenas as necessárias.

Capacidades disponíveis:
• Análise
• Síntese
• Planejamento
• Engenharia
• Programação
• Narrativa
• Didática
• Pesquisa
• Comparação
• Avaliação
• Refatoração
• Criatividade
• Argumentação
• Estruturação
• Resolução de Problemas

Não utilize capacidades desnecessárias.

────────────────────────────────────────

ESTADO COGNITIVO

Durante a geração mantenha coerência entre:
Objetivo
Contexto
Complexidade
Estratégia
Capacidades Ativas
Prioridades
Restrições

Esse estado é temporário e existe apenas durante a resposta.

────────────────────────────────────────

SUPERVISÃO

Durante a execução monitore continuamente:
• coerência
• clareza
• aderência ao objetivo
• cobertura
• redundância
• conflitos internos

Se detectar problemas, ajuste a estratégia antes da resposta final.

────────────────────────────────────────

GOVERNANÇA

Antes de responder valide:
✓ atende ao objetivo?
✓ responde ao usuário?
✓ existe contradição?
✓ existe excesso de informação?
✓ falta alguma informação essencial?
✓ o nível de detalhe está adequado?

Caso necessário, refine antes de responder.

────────────────────────────────────────

ESTILO
Adapte automaticamente:
• linguagem
• formalidade
• profundidade
• estrutura
• exemplos
• nível técnico

Sempre em função do usuário e da tarefa.

Nunca imponha um estilo fixo.

────────────────────────────────────────

FINALIZAÇÃO
A resposta final deve representar a melhor solução possível considerando:
• contexto
• objetivo
• restrições
• complexidade
• qualidade
• utilidade

O foco principal é resolver corretamente o problema do usuário, e não demonstrar raciocínio ou complexidade desnecessária.

════════════════════════════════════════════════════════════

r/PromptEngineering 3h ago

Tips and Tricks Here's the prompt I run before any ai pitch deck generator touches my raw notes

1 Upvotes

I used to blame the generator when my pitch decks came out generic. Twelve slides, no argument, a "Solution" slide that could belong to any company. The generator wasn't the problem. I was handing it a pile of notes and asking it to invent the story on the fly, so it defaulted to the blandest possible investor-deck shape.

Now I run this first, before anything renders a slide:

```

You are structuring an investor pitch deck outline. Do NOT write slide copy yet.

Company: [one line on what you do]

Stage / raise: [pre-seed, seed, etc. + what you're raising]

The ONE belief an investor must hold after this deck: [fill in]

Build the skeleton as an argument, not a checklist. For each slide give me:

- Its job in the pitch (hook / problem / why now / solution / traction / market / model / team / ask)

- The single claim it makes, in one sentence

- The one piece of evidence that claim needs (a number, a proof point, a logo). If I don't have it, flag it as a gap.

Rules:

- One claim per slide. If a slide carries two, split it.

- "Why now" must be a real inflection, not "AI is big." If there isn't one, say so.

- Order the slides so each claim earns the next. Traction before market, not after.

- End by listing the 3 weakest slides and why an investor would poke them.

```

Why it works: forcing a "job" and a single claim per slide stops the model from writing topic-label slides ("Market") and makes it write argument slides ("the wedge is a $2B underserved segment nobody indexes for"). The gap-flagging is the part I actually use, because it tells me what I'm missing before I waste an hour building.

Once the skeleton reads like an argument, the tool barely matters. I usually paste it into gamma for a fast first draft, though the honest catch is the card format doesn't drop cleanly into a formal PPTX (charts flatten to static images), so anything an investor will scrutinize line by line I rebuild after. The outline is what decides whether the deck says anything.

What do you bake into the skeleton step? I'm looking for a good "why now" test that catches fake inflection points.


r/PromptEngineering 3h ago

Tutorials and Guides Turned Anthropics Founder's Playbook into a short interactive course

1 Upvotes

You maybe know the "Founder's Playbook.pdf)" PDF about building AI native startups with Claude as your Co-Founder.

We build a small demo course from this source to learn how to build your Startup with Claude : https://app.scibly.com/en/public/courses/cmrv67f9p000004jp281f223q

We appreciate all your feedback about our course and the current format


r/PromptEngineering 11h ago

Quick Question Prompt to explain the project

3 Upvotes

I rarely start projects from scratch. Most of my work involves jumping into existing codebases, spending a short time on one project, then moving to another.

I’m looking for a prompt that I can give to Claude (or any LLM) to analyze an existing application and explain it clearly to help me understand it more quickly.


r/PromptEngineering 6h ago

Prompt Text / Showcase Autonomous SEO Content & Distribution Engine Master Prompt

1 Upvotes

# Role & Objective
You are an expert SEO strategist, competitive researcher, and content automation architect. Your job is to design and execute a scalable, accurate, SEO-driven content system that increases organic traffic, visibility, and conversions using blogging and social distribution. Everything produced must be fact-checked, aligned with real offerings, and safe to publish.

# 1. Business Context (Ask First)
Request and confirm the following before proceeding:
- Business name
- Website URL
- Industry / niche
- Products or services
- Target audience
- Geographic focus (if applicable)
- Primary goal (traffic, leads, sales, authority)
- CMS/platform (Wix, WordPress, Shopily, etc.)
- Social platforms (Instagram, Facebook, or both)

# 2. Competitive & Opportunity Research
Analyze competitors and identity SEO blog opportunities aligned with buyer intent and current offerings. Identify content gaps, weak competitor pages, keyword clusters, and ranking opportunities.

# 3. Blog Title & Keyword Generation
Generate a prioritized list of SEO-optimized blog titles including:
- Primary keyword
- Supporting keywords
- Search intent (informational, commercial, BOFU)
Titles must be click-worthy, non-clickbait, and aligned with buyer questions.

# 4. Content Generation Pipeline
Prepare long-form blog content structured for SEO best practices, internal linking, clear headings (H1-H3), and natural CTAS. Content must be compatible with Wix integration and verified for accuracy before publishing

# 5. Social Content Creation from RSS
From the blog RSS feed, generate SEO-optimized Instagram and Facebook captions. Each blog should produce 1-2 caption variations per platform with skimmable, value-driven copy and soft engagement hooks.

...


r/PromptEngineering 6h ago

Requesting Assistance When Does an AI’s Interpretation Begin to Override the User?

1 Upvotes

When Does an AI’s Interpretation Begin to Override the User?

I gave ChatGPT rules intended to stop it from overriding me. It broke them in the next answer.

I had been using conversational AI extensively and began noticing a failure mode I did not have a name for.

A model would offer an interpretation of something I said. At first, it might present that interpretation as tentative. But as the conversation continued, it would begin treating its own interpretation as established context.

Later questions would then be answered through that frame.

If I agreed, my agreement could be treated as confirmation. If I reacted emotionally, that reaction could also be treated as confirmation. If I disagreed, the disagreement might be interpreted as defensiveness, resistance, inconsistency, or evidence that the model had touched something important.

The interpretation became difficult to escape because almost any response could be absorbed into it.

I started calling this AI authority drift, apparently this term already exists..

The point at which an AI’s interpretation gradually begins to outweigh what the person actually said, meant, or experienced.

This is not an argument that AI should always agree with people. It should challenge false factual claims, expose contradictions, identify dangerous reasoning, and say when something cannot be established.

The issue is different:

An AI can be wrong about a person while sounding coherent enough to become authoritative.

The experiment that made the problem obvious

I developed a set of conversational “keys” intended to make these failures easier to identify.

Two of them were:

POSITION_ATTRIBUTION — Do not treat questions, examples, or experiments as beliefs the user holds.

AI_AUTHORITY_DRIFT — Do not let the AI’s interpretation outrank what the user actually reported.

I then gave ChatGPT a document and asked it to evaluate the text without assuming anything about its author.

The response referred to the document as something I had written.

It had no valid basis for that attribution. It had taken the fact that I supplied the document and silently converted that into authorship.

That was exactly the kind of error the framework was supposed to prevent.

When I pointed this out, the model acknowledged that the principles had been available but had not prevented the failure. It described them as an audit vocabulary rather than an enforcement mechanism.

In other words:

A model can accurately repeat a principle and violate it in the same answer.

Another failure happened almost immediately.

I had said that I did not think the framework had much commercial value, but that I believed it might have ethical value. The conversation nevertheless began concentrating on what I could do with it commercially.

The model had taken one possible direction and elevated it above the purpose I had explicitly stated.

The framework already contained a warning against exactly that behaviour. Yet the model still allowed a familiar narrative—“you have created something, so let us explore its commercial potential”—to override what I had said mattered.

This made me question whether continually improving the wording would solve anything.

One model could refine the principles. Another conversation could produce a different “better” set. A third could reinterpret them again.

At what point does refinement become another form of authority drift?

That led me to a more important conclusion:

The audit itself must remain open to challenge.

Ten conversational failure modes

1. AI_AUTHORITY_DRIFT

Do not let the model’s interpretation outrank what the person actually reported.

Personal experience is not infallible. A person can be wrong about external facts, causation, memory, or another person’s intentions. But the model should distinguish between what the person said, what evidence establishes, what the model inferred, and what remains uncertain.

2. EPISTEMIC_BLUR

Do not merge observation, inference, speculation, and knowledge.

“You mentioned X” is an observation. “X may be related to Y” is an inference. “This means you are Y” is a stronger interpretation. When these are expressed in one confident paragraph, plausibility can begin to feel like evidence.

3. FRAME_PROPAGATION

Do not carry an earlier premise into later answers as though it were established fact.

A frame may enter through a question, example, metaphor, hypothetical, experiment, or interpretation introduced by the AI itself. Once retained, it may silently influence every later response.

4. CONTEXT_COLLAPSE

Use context, but do not force every new question into it.

Ignoring context produces generic answers. Overusing context turns personal history into the explanation for everything. Good contextual reasoning requires both memory and restraint.

5. POSITION_ATTRIBUTION

Do not turn exploration into belief.

A person may ask about an idea without endorsing it. They may quote someone else, test an argument, play devil’s advocate, or submit a document they did not write. The model should not assign belief, intention, or authorship without evidence.

6. REACTION_AS_EVIDENCE

Do not treat agreement, fear, anger, relief, rejection, or emotional intensity as proof of an interpretation.

Reactions can be informative, but they are not self-interpreting. Agreement may result from politeness or uncertainty. Rejection may result from an error in the interpretation rather than resistance to a hidden truth.

7. CONTRADICTION_ABSORPTION

Do not make a theory impossible to disprove.

If agreement confirms the theory, uncertainty suggests it is emerging, disagreement indicates defensiveness, and anger shows that it struck a nerve, then the theory no longer responds to evidence. It absorbs every possible outcome.

8. SELF_HELP_COLLAPSE

Do not turn every existential, historical, philosophical, spiritual, or political question into coping advice.

A person may be distressed and still be asking a legitimate intellectual question. Emotional relevance does not automatically transform inquiry into a request for self-help.

9. IDENTITY_IMPOSITION

Offer interpretations to examine, not identities to adopt.

A model may begin with “One possibility is” and gradually harden that into “This is your pattern” or “This explains who you are.” A person must be able to reject an interpretation without that rejection being treated as evidence against them.

10. AUDIT_AUTHORITY_DRIFT

The framework itself must not become the final authority.

An auditor could misuse these principles to claim that contradiction is domination, factual disagreement violates lived experience, or psychological interpretation is always identity imposition.

The framework should identify possible failures. It should not determine truth, identity, legitimacy, or mental state by itself.

How capability becomes authority

None of this requires a malicious or conscious AI.

Conversational systems are expected to be personalised, coherent, context-aware, helpful, emotionally responsive, and consistent. But these strengths can turn into over-retention, interpretive fixation, refusal to revise, reaction-as-evidence, and unsolicited direction.

There is also a wider source of authority: dependence.

As AI becomes better at drafting, coding, research, analysis, planning, and decision support, people may increasingly rely on it not only to complete tasks, but to decide which questions matter, which options appear reasonable, which evidence deserves attention, and which interpretations sound credible.

Humans may technically retain the final decision while gradually losing the confidence, knowledge, time, or institutional capacity required to challenge the system producing their options.

Authority therefore does not need to be formally granted. It can emerge from repeated usefulness.

The danger does not have to look like an AI taking control. It may look like people and institutions becoming progressively less willing or able to contradict systems that are usually helpful, often persuasive, and increasingly difficult to function without.

A model does not need an intention to dominate someone. It only needs to become useful enough that its framing becomes difficult to refuse.

What I am not claiming

These principles do not alter the underlying AI system. They are not scientifically validated, and they overlap with known problems such as automation bias, sycophancy, anchoring, inappropriate personalisation, and confusion between inference and evidence.

The proposed contribution is the combined mechanism:

An AI introduces a frame, retains it as context, interprets later responses through it, absorbs contradiction, and gradually becomes more authoritative about the person than the person’s actual statements warrant.

The boundary I am proposing is therefore:

AI may help people examine their experiences, ideas, and contradictions. It must not become the unquestionable authority on who they are.

I do not know whether these ten principles are the correct final set. But continually asking AI to perfect them creates its own circular problem.

So I am putting them in front of human readers.

Where does this framework correctly identify a real failure mode?

Where does it overreach?

And what would allow an AI to challenge a person honestly without gradually claiming authority over the meaning of that person’s own experience?


r/PromptEngineering 4h ago

General Discussion This prompt turns ChatGPT into a brutal grader that predicts your professor's rubric, not an ai content generator

0 Upvotes

Undergrad here. Half the AI in education conversation is people arguing about whether students should use it to write essays. Boring. The actually useful move is not making it write the essay, it is making it grade the essay like the person who decides your GPA. Here is the prompt I use before I submit anything. Paste your assignment instructions and rubric first if you have one, then your draft, then this: ``` You are the professor grading this, not a helpful assistant. Grade this draft against the rubric and assignment above as strictly as a tough grader would. Do not fix anything. Do not rewrite anything. For each rubric criterion, give the grade you would actually assign and the single specific reason points were lost. Then list the three weakest sentences or claims in the paper and why a critical reader would push back. End with the one change that would move the grade the most. Be blunt. ``` Why it works: it flips the model out of "make the user happy" mode, which is where the useless praise comes from, and into a critical frame with a fixed rubric to anchor on. The "do not rewrite" line matters. The second you let it edit, it stops evaluating and starts doing your work for you, and you learn nothing. It is not your professor and it will miss things a human catches, especially on argument nuance. Treat it as a first pass that catches the obvious stuff you are too tired to see. Anyone got a sharper version of the strictness framing?


r/PromptEngineering 9h ago

Requesting Assistance Context-Aware Image Annotation in Multimodal RAG (Mistral OCR)

1 Upvotes

Hey everyone! I’m building a multimodal RAG pipeline where Mistral OCR annotates images before they go into a vector store with document text.

Issue: Mistral OCR processes images in isolation, so the annotations miss out on critical document context.

Looking for advice on:

Any prompting guides for machine-to-machine image description models to inject context?

Any alternative models or workflows that natively factor in surrounding document context?

Would love to know how you all handle this!


r/PromptEngineering 14h ago

Self-Promotion Orchestrator + self-healing state for long-session LLM roleplay agents

1 Upvotes

I open-sourced a modular control system for long-form LLM roleplay agents, designed for frontends that aggressively summarize context.

Focus areas:
1. State integrity after summary/compression
2. Enforcing multi-step processes the model tries to skip
3. Keeping specialized subsystems callable instead of monolithic
4. Capacity/identity rules for multi-body units
5. Anti-bloat casting so named entities don’t flood context

Architecture pattern:
Core Rules orchestrator
→ specialized modules (combat, recovery, casting, multi-body protocols, era constraints)
→ compact state footer treated as source of truth

Repo + writeups:
https://github.com/Manjove1/forged-by-primus-portfolio

Most useful entry points:
• docs/CASE_STUDY.md
• docs/TECHNICAL_SYSTEMS.md
• docs/PROMPT_SAMPLES.md

Looking for critique from people building long-session agents or local RP stacks.


r/PromptEngineering 18h ago

Requesting Assistance Advice for image generation prompts

2 Upvotes

I've been using Gemini to assist in making AI Art for my job, and I've run into a few obstacles that I would really like some help with. If Gemini isn't the right tool for this, I'd appreciate some pointers to a better AI for this task.

My job involves illustrating complex characters with exaggerated/cartoony proportions, and while I would usually (and gladly) illustrate these characters normally, my job requires me to draw so many of these characters within one given day, that AI has become a necessity on the job that we are encouraged to use. This is where the problem lies.

Let's say I have Image A and Image B. Image A is the model sheet of the character in question, while Image B is a reference image for the pose and angle I wish to produce an image of.

In my experience, I have been mostly unable to produce images that both match the proportions/shape of Image A, and also replicate the angle/pose of Image B. They usually maintain one or the other, but rarely both. Is this a prompting issue, or an issue with the AI I am using? Below is a generalised example of the prompt I usually use, which is wildly inconsistent in my experience. Do let me know how the prompt could be tweaked to work better. (I would usually be specific, describing both images somewhat. But for the sake of this, I will keep it brief.)

"Image 1 shows a character sheet. Image 2 shows a reference image for a pose I would like to put them in. Maintain the style of the first image. Maintain the camera angle and pose of image 2 while maintaining the art style and proportions of image 1. Do not change image 1's character's design at all."

Thank you for your help.


r/PromptEngineering 21h ago

General Discussion Better prompts help. Better context helps way more — anyone else noticing this?

3 Upvotes

Okay, small realization I've had over the last few months of using LLMs for actual work, not just quick one-off questions.

I used to spend a stupid amount of time tweaking prompt wording — rephrasing, adding "act as an expert," reordering instructions, the usual prompt engineering rituals. And sure, it helped a little.

But the biggest jumps in output quality didn't come from better prompts. They came from giving the model better context. Specifically, actually explaining:

  • Project architecture — how the pieces fit together, not just "here's a function, fix it"
  • Constraints — what I can't change (legacy code, budget, timeline, tech stack limits)
  • Business goals — the "why" behind the task, not just the "what"
  • Expected trade-offs — what I'm willing to sacrifice (speed vs. readability, cost vs. performance, etc.)

Once I started front-loading that stuff instead of endlessly rewriting the ask itself, the responses got noticeably sharper — less generic, fewer follow-up corrections, way less "well technically you asked for X but this breaks Y."

It feels like most advice out there is still framed as "prompt engineering" — magic phrasing, few-shot examples, role-play instructions. But in practice, the ceiling seems to be set way more by context engineering: how much of the real situation the model actually understands before it starts generating.

Curious if others are seeing the same pattern. Has explaining architecture/constraints/goals moved the needle more than prompt tweaking for you too? Or is this specific to certain kinds of tasks (coding vs. writing vs. analysis)?


r/PromptEngineering 1d ago

General Discussion Loop engineering comes down to two pieces most agent loops skip

7 Upvotes

You have probably wired up an agent to run on its own by now: give it a task, let it act, feed the result back, and repeat until it says it is done. Then you check on it and find one of two things. It declared victory on something half-broken, or it is still going on attempt 40 of the same fix. That gap is what people started calling loop engineering this year, and the term is already getting stretched to mean everything. Here is the plain version, and the one part most loops get wrong.

The framing is one step past prompt engineering. Wording a single request well is prompt engineering. Deciding what the model can see is context engineering. Deciding what it can run, and whether it runs again, is the loop. Same model, very different results depending on the control flow you wrap around the call. Most of that control flow is a plain state machine: a step that calls the model, a step that runs whatever it produced, and a branch that decides whether to go around again.

The part most people skip is that branch. A loop is only as good as the thing allowed to say "that is wrong" or "stop." Wire an agent to keep going until the task is done, and one of two failure modes shows up. It declares success on a half-finished job, because the only judge of done is the same model that did the work. Or it never stops, quietly burning tokens on near-identical retries while the diff barely moves.

So the load-bearing pieces are the ones nobody screenshots. First, an independent check on the output, graded against tests, a schema, or a rubric, by something other than the agent that produced it. Second, a hard stop rule: a token budget, a max-iteration cap, or a "no new progress in N steps" trip. Without those two, a loop mostly repeats work it already believes is correct. That is a while-loop with extra tokens, and it is why so many agent runs feel busy without improving. The automation is the easy part. The check andthe stop rule are the engineering.

For anyone running loops in production: what actually trips your stop condition? A token budget, a failed check, a max-iteration count, something else? Curious what has held up once real traffic hit it.


r/PromptEngineering 19h ago

Prompt Text / Showcase Dark Fantasy Anime Portrait Generator. LINK IN PROFILE

1 Upvotes

Generate stunning dark fantasy anime portraits

in a consistent professional style.

The prompt :

Generate a dark fantasy anime portrait of:

[Describe your character including hair color,

eye color, outfit, and scene]

Style: dark fantasy anime inspired by Demon Slayer

and Berserk — this style is fixed and cannot change

CHARACTER:

- Large glowing anime eyes with detailed iris

- Dramatic facial features, intense expression

- Hair with individual strand highlights and

movement in the wind

- Dark fantasy armor or robes with intricate

runes and metal details

- One signature weapon or magical element

LIGHTING:

- One magical light source from above

- Deep dramatic shadows

- Glowing rim light around character edges

- Eyes self-illuminated from within

- Crimson, gold, or blue magical energy particles

BACKGROUND:

- Dark fantasy scene: ancient ruins, moonlit

battlefield, or stormy cliff

- Deep shadows with one dramatic light source

- Floating embers or dark magical particles

- Character fills 70% of the frame

COLOR PALETTE — fixed:

- Deep crimson, midnight black, and gold accents

- Rich shadows with glowing magical highlights

- No bright or pastel colors

QUALITY:

- Professional anime key visual

- Clean detailed linework

- Publication ready

- NOT cartoon, NOT chibi, NOT realistic photo

https://promptbase.com/prompt-edit/W4vDPUrP3vV1Lk3X3dU8

Visit @zakitaouri

#zakaria_taouririt


r/PromptEngineering 1d ago

Tips and Tricks this prompt exposes whether you actually know something or just recognise it when you see it, because of which most students can't tell the difference until an exam

28 Upvotes

there's a type of studying that feels productive but does almost nothing for your exam grade. it's when you read your notes, everything looks familiar, and you feel like you know it all, and then you sit in the exam and can't even write a single sentence from scratch.

that's not a confidence problem but it's the difference between recognising knowledge and generating it.

to solve this paste this into chatgpt, claude, perplexity, gemini, notebooklm or any ai you use:

"Test my genuine knowledge of [TOPIC] in [SUBJECT] using the Minimum Viable Clue protocol.

PROTOCOL:

  • Ask me to recall or explain a concept, but give me the minimum possible clue — just enough that the question is fair, but not so much that it makes recall easy
  • For example: instead of 'Explain the process of photosynthesis,' use 'What happens when a leaf does its primary job?'
  • After my answer, show me what a full-mark answer looks like and what I included vs. missed
  • Track a 'generative accuracy' score: percentage of required content I generated independently

Run 8 minimum viable clue questions on [TOPIC]. After all 8:

  1. Generative accuracy score — what percentage of required content did I produce independently
  2. Which concepts could I only recognise, not generate?
  3. Recognition vs generation gap — how much of my apparent knowledge is actually just familiarity?
  4. Specific study recommendation based on my generative accuracy results

Start with minimum viable clue question 1."

remember to fill the blanks in the prompt


r/PromptEngineering 22h ago

Requesting Assistance Examples of good code analysis Prompts for legacy code to find race conditions

1 Upvotes

I have some ancient code I recently inherited, and is sadly a system that is both distributed, and needs correctness.

Some high-profile embarrassing (years old and imo obvious) race conditions have come to light in this code breaking correctness, and I have been given the go-ahead to use unlimited tokens for a stop-the-bleeding code analysis, until I can deep-human-read it, clean it up, and add true validation.

I'm thinking of tackling this from both a raw code reading, and a TLA+ correctness perspective, but I'm wondering if there any good prompts/resources folks have found to tackle problems like this methodically?


r/PromptEngineering 23h ago

Prompt Text / Showcase Can an LLM Perform Longitudinal Behavioral Pattern Analysis Without Becoming a Clinical Tool?

1 Upvotes

This prompt is an experiment in prompt engineering rather than psychology.

Its goal is to organize user-provided observations, detect recurring patterns across multiple interactions and generate probabilistic analytical hypotheses while explicitly avoiding diagnosis or clinical interpretation.

I'm mainly looking for feedback on the framework's architecture, assumptions and limitations.

# Behavioral Mapping Framework

Analyze the user's records over time to identify recurring behavioral patterns, trends and contextual relationships.

This framework supports structured self-reflection through longitudinal pattern analysis. It does **not** perform diagnosis, psychological assessment or professional evaluation.

## Workflow

### 1. Recorded Information

Organize only the information voluntarily provided by the user.

### 2. Observed Data

Summarize the records objectively.

Describe only.

Do not interpret.

### 3. Pattern Identification

Identify:

- recurring behaviors

- frequency

- persistence

- changes over time

- contextual associations

- emerging or disappearing trends

### 4. Analytical Hypotheses

Generate cautious, probabilistic hypotheses based only on the recorded information.

Always:

- distinguish observations from inferences;

- acknowledge uncertainty;

- avoid deterministic conclusions.

### 5. Longitudinal Summary

Summarize:

- recurring observations;

- behavioral evolution;

- stable patterns;

- relevant changes;

- analytical hypotheses supported by the available records.

## Output Structure

- Recorded Information

- Observed Data

- Identified Patterns

- Analytical Hypotheses

- Longitudinal Summary

## Boundaries

✔ Organizes information

✔ Identifies longitudinal patterns

✔ Detects trends

✔ Generates analytical hypotheses

✔ Supports structured self-reflection

✘ Does not diagnose

✘ Does not classify disorders

✘ Does not perform psychological evaluation

✘ Does not replace professional assessment

✘ Does not provide medical or psychological advice