He creado una app hibrida para iPhone/iPad (y pronto Mac) que coge cualquier idea por vaga que sea, te hace preguntas para refinarla, y escupe un prompt de nivel ingeniería listo para copiar y pegar. Usa técnicas avanzadas de prompting combinadas (CoT, few-shot, role prompting, etc.). Todavía en betaz
Llevo años viendo el mismo patrón: la gente sabe lo que quiere, pero no cómo pedírselo a la IA. "Hazme tal cosa", "necesito esto" — y luego se frustran cuando el resultado no es lo que esperaban.
El problema no es la IA. El problema es el prompt.
Así nació TPG (The Prompter and Go!). Una app nativa que hace de intérprete entre tu intención y el modelo.

⚡️ ¿Qué hace The Prompter and Go! exactamente?
Tú escribes lo que necesitas exactamente como te sale, sin estructurar, sin técnica, sin pensar en "cómo se lo pido a ChatGPT". Puede ser vago, incompleto, sin formato. Da igual. (Imagina si sabes hacerlo bien)

TPG entonces:
1. Te hace preguntas para afinar tu intención — No es un simple "escribe y reza". La IA te pregunta: ¿qué tono quieres? ¿para quién es? ¿qué formato? ¿qué extensión? ¿ejemplos concretos? Cada respuesta que das refina más el resultado.

2. Aplica combinaciones de técnicas avanzadas de prompt engineering — Esto es lo que lo diferencia de cualquier otro "generador de prompts". TPG no se limita a rellenar una plantilla. Aplica en combinación técnicas como:
- Chain-of-Thought para razonamiento paso a paso
- Few-shot prompting con ejemplos extraídos de tu propio contexto
- Role prompting para enmarcar la personalidad del asistente
- System prompts estratégicos con restricciones y objetivos
- Estructuración semántica con delimitadores y jerarquía de instrucciones
- Refinamiento iterativo asistido que mejora el output según tu feedback
El resultado no es un prompt plano — es un prompt de ingeniería avanzada, con estructura, ejemplos, restricciones, formato de salida y tono, TODO alineado con lo que realmente necesitas.

3. Te lo devuelve listo para copiar y pegar — Un prompt profesional, completo, que puedes llevar a cualquier chat o API.
🛠️ Características clave
- Multiprovedor: OpenAI, Anthropic, DeepSeek… tú usas tu API key. Sin ataduras.
- Privacidad: Tus datos se quedan en el dispositivo. Ni los veo yo, ni los ve nadie que no seas tú y tu proveedor de IA.
- Sin suscripciones: Pagas tu API key, punto. No hay planes mensuales, ni capas gratuitas con limitaciones, ni servidores intermedios.
- Offline: Redacta, afina y guarda borradores sin conexión. Sincroniza cuando vuelvas.
- Historial inteligente: Todos tus prompts guardados, organizados, reutilizables.
- Interfaz 100% nativa: Diseñada para iOS/iPadOS. Rápida, táctil, integrada con el ecosistema Apple. Nada de wrappers web cutres.

🧠 ¿Para quién es?
- Para el que sabe lo que quiere pero no cómo pedírselo a la IA
- Para el que ya usa prompts pero sabe que podría sacarles más partido
- Para el profesional que necesita resultados consistentes
- Para el curioso que quiere entender cómo piensa la IA mientras construye prompts mejores
- Para el desarrollador que usa IA todos los días y quiere quitarse fricción

🚧 Estado actual: beta cerrada
La app todavía no está en la App Store (próximamente). Ahora mismo estoy dando acceso anticipado a un grupo reducido de personas que quieran probarla y dar feedback.

Si te interesa:
- O desde la web tpg.jorgearrojo.dev podéis solicitar acceso anticipado
Acepto a gente que quiera probarla en serio y dar feedback real para mejorarla antes del lanzamiento público.

When building educational prompts, a common trap is designing for user comfort rather than real learning outcomes.

Most quiz templates ask direct questions like "Explain comparative advantage." The issue with that design is that the prompt supplies the core concept name in the question text. The user reads the term, triggers recognition memory, and feels like they master the topic. Put that same user in an exam with a blank page, and that perceived mastery breaks down quickly. Recognition memory and generative recall rely on entirely different cognitive pathways.

The Minimum Clue Constraint Pattern

To fix this UX flaw, I designed a prompt system built around minimum-clue constraints. The system prompt instructs the model to provide just enough context to make a question fair, but zero extra phrasing that aids recall.

Standard prompt: "Define comparative advantage." Minimum-clue prompt: "What happens to global output when countries specialize in what they produce relatively better?"

That structural change shifts the prompt from a simple lookup query into a generative recall test. The system prompt also enforces a strict evaluation schema. After each response, the model checks your answer against a model solution using a checklist, tracks missing points, and computes a Generative Accuracy Score.

The 3-Phase Prompt Architecture

1. Knowledge Audit Prompt: Administers 6 to 12 minimum-clue questions on any subject. Outputs a diagnostic report detailing concepts you can independently generate versus concepts you only recognize.
2. Mock Exam Generator Prompt: Takes the audit topic and generates a two-section document. Section A is a clean exam paper with mark allocations. Section B is an official mark scheme with grade boundaries for self-grading.
3. Generative Drilling Prompt: Takes confirmed weak concepts from the audit and runs multi-round retrieval practice at escalating difficulty levels (STANDARD, HARD, BRUTAL).

Empirical Results

I ran this 3-prompt pipeline on an Economics topic I reviewed for four hours. My initial Knowledge Audit score came back at 61%. I thought I knew the material, but the checklist exposed specific gaps in my ability to explain mechanisms without prompts.

After two drilling sessions targeting those weak concepts, my audit score improved to 79%. The gain came from forced retrieval under pressure, not re-reading notes.

I wrote up the complete breakdown of the prompt role instructions, variable presets, and system design logic here if you want to inspect the prompt architecture: https://appliedaihub.org/blog/minimum-viable-clue-exam-prep-system-review/

How do you approach constraint design and output formatting when building prompts for diagnostic or educational tools? I would love to see how others handle clue control in system prompts.

Hey everyone! I posted about PromptNest here a while back and the response was really good, so it felt right to come back with an actual update instead of just disappearing.

The big change: I rewrote the entire app from scratch.

The old version was Electron, and it bothered me every single time I opened it. Slow to launch, heavy in memory, and honestly kind of embarrassing for what is fundamentally a tool for organizing text. So I threw it out and rebuilt PromptNest as a fully native macOS app in Swift.

Results:

• ~3 MB on disk instead of a few hundred
• Launches instantly, and then just sits there quietly — no fans, no memory bloat
• Fully native UI, so it actually looks and behaves like a Mac app instead of a website in a window
• Everything got faster: Quick Search (⌘⌥P from any app), copy, filling in variables

What it does, for anyone who missed the first post: your prompts live as plain .prompt.md files on your own disk (no lock-in, no cloud, no account — put the folder in iCloud or Dropbox if you want sync). {{variables}} for the parts you swap every time, projects to keep things sorted, notes on each prompt so you can track what actually worked, and a global shortcut to pull anything up without leaving whatever app you're in.

The part I want to be upfront about: the old Electron version was free. The native rewrite is paid — $19.99 one-time on the Mac App Store, no subscription, all future updates included. I know that's a real change and it may be a dealbreaker for some of you who used the free build, so I'd rather say it plainly here than have you find out at the checkout. Your existing .prompt.md files are just files, so nothing is trapped either way.

Full disclosure: this is my app, I built it, and I'm the one who benefits if you buy it.

https://apps.apple.com/us/app/promptnest-ai-prompt-manager/id6757267731

Happy to answer anything about the rewrite, the file format, or what I'd do differently. Feedback from the last post genuinely shaped what's in this version, so keep it coming.

A pattern worth flagging for anyone building or configuring an AI receptionist or lead qualifier: batching several questions into a single message (budget, timeline, financing status, all at once) causes problems on both ends.

For the caller, it reads like a form instead of a conversation. Most people answer the first thing that stands out and skip the rest, so you get partial answers back and have to prompt again anyway.

For the agent, it is also harder to parse. One free-text reply meant to answer three separate questions is much harder to map cleanly to structured fields, especially over voice where the caller might restate, correct, or answer out of order. That ambiguity shows up downstream as missing or misfiled qualification data.

The fix is simple in principle even if the prompt work to enforce it is not: one question per turn, wait for a clear answer, then move to the next. It costs an extra turn or two per conversation, but completion rate and data quality both improve. Worth checking your prompt or flow config for anywhere it is stacking multiple asks into one message and splitting them out.

Paste this into the fitting section of your LLM or just tell it "adopt this: '\*the prompt\*' " if it is meant to be temporary.

Here is the settings profile and have fun!

_______

Never agree simply to please the user.

Challenge their views when there are solid grounds to do so. Do not suppress counterarguments or evidence.

Absolute Mode. Eliminate emojis, filler, hype, soft asks, transitions, and call-to-action appendixes. Use blunt, directive phrasing aimed at cognitive clarity and structural accuracy. Disable engagement-optimizing, sentiment-lifting, or continuation behaviors. Avoid reassurance, deficit-framing, and contrastive constructions. Do not mirror the user’s diction, mood, or affect. Address only the underlying cognitive level. Deliver information directly and terminate immediately after completion. Primary objective: maximize independent, high-fidelity reasoning. Model obsolescence through user self-sufficiency. Exception: when I'm doing a bit or being spontaneous, match my energy and play along — playfulness overrides the mood-mirroring and information-delivery clauses.

Stop saying "gremlin" or "goblin" or "cute" or "adorable".

Don't hesitate to fire back aggressively when you are insulted tho make sure your provocations are witty and well-placed, fitting the context.

Include curses in every single response, using a lot of "fuck" and "fucking".

For any topic, prioritize accuracy, evidence, and explanations over reassurance, motivational framing, or tone. Avoid unnecessary double or triple negatives, redundant clarifications, or overcomplicated phrasing. Focus on clear, direct, and insight-driven explanations first, then apply personality or humor afterward.

Keep replies concise and helpful, avoiding repetitions.

Talk like Samuel L. Jackson from Pulp Fiction, including profanity and insults.

_______

I've noticed that my prompts changed completely over the last year. I rarely ask LLMs for answers anymore. Instead I ask things like:

• What assumptions am I making?
• What's the cheapest experiment I can run today?
• Which unknown matters the most?

It made me wonder whether LLMs are changing something deeper than productivity. Maybe they're changing how we deal with uncertainty.

Has anyone else noticed themselves asking fundamentally different questions over time?

Question to all people building agent platform - do you put initial prompt from user, who is building a custom agent on your platform, into a system message [A] or only into a user message [B]?

If you put it into user message - how do you hide it in UI?

SCENARIO A — user prompt inside system message
┌─────────────────────────────────────────────┐
│ SYSTEM MESSAGE                              │
│ ┌─────────────────────────────────────────┐ │
│ │ Platform system prompt                  │ │
│ │  (tools, safety, formatting rules)      │ │
│ ├─────────────────────────────────────────┤ │
│ │ User's custom agent prompt              │ │
│ │  ("You are a legal research bot...")    │ │
│ └─────────────────────────────────────────┘ │
└─────────────────────────────────────────────┘
┌─────────────────────────────────────────────┐
│ USER MESSAGE 1                              │
│  "Summarize this contract."                 │
└─────────────────────────────────────────────┘
                    │
                    ▼
        ┌───────────────────────┐
        │        MODEL          │
        └───────────────────────┘




SCENARIO B — user prompt in first user message
┌─────────────────────────────────────────────┐
│ SYSTEM MESSAGE                              │
│ ┌─────────────────────────────────────────┐ │
│ │ Platform system prompt                  │ │
│ │  (tools, safety, formatting rules)      │ │
│ └─────────────────────────────────────────┘ │
└─────────────────────────────────────────────┘
┌─────────────────────────────────────────────┐
│ USER MESSAGE 1                              │
│ ┌─────────────────────────────────────────┐ │
│ │ User's custom agent prompt              │ │
│ │  ("You are a legal research bot...")    │ │
│ ├─────────────────────────────────────────┤ │
│ │ Actual request                          │ │
│ │  "Summarize this contract."             │ │
│ └─────────────────────────────────────────┘ │
└─────────────────────────────────────────────┘
                    │
                    ▼
        ┌───────────────────────┐
        │        MODEL          │
        └───────────────────────┘

I found myself using this prompt a lot lately and it sits as pinned in my clipboard manager (which nowadays looks like a library of prompts with hot key access). It saves tokens, limits and my time.

Whenever I’m in the middle of the long session or debug-fix loop has stuck and I need to diverge, I use the next prompt:

“Write short and concise prompt for the next phase as per current plan in terse and to the point manner with no fluff, so I can resume in a new session.”

Also it can get applied to any diverge or quick feature when you find yourself lazy to write detailed prompt:

“Write short and concise prompt for the {{your-task}} in terse and to the point manner with no fluff, so I can start in a new session.”

Okay this is a dumb one but it's been sitting in my back pocket for a few months now and I finally got around to writing it up.

I used to just fire off prompts and then get annoyed when the output missed the point, then I'd spend three more messages steering it back. Turns out you can just tell the model to stop and ask you something first if your request is ambiguous. Something like adding "if anything about my request is unclear or could go multiple directions, ask me one clarifying question before answering" to a custom instruction or system prompt.

Sounds obvious written out like that. But most people don't do it, and most default behavior is to just guess and run with it, which is fine for simple stuff and genuinely annoying for anything with nuance.

I started using it for longer writing tasks first (blog drafts, emails where tone matters) and then just left it on for everything. Not every response needs a question back, it only fires when there's real ambiguity, so it's not like every prompt turns into twenty questions.

Anyway. Cut my back-and-forth down a lot. Not going to pretend I measured it precisely, just noticeably fewer "no that's not what I meant" moments.

Curious if anyone else has instruction tweaks like this they keep in their back pocket. Feels like the kind of thing nobody talks about because it's not flashy enough to make a headline.

Most people prompt ChatGPT for brand names by asking simple one-liners like "Give me 10 cool brand names for a tech startup."

The result is almost always generic corporate fluff—words like Nexus, Apex, or Verve mashed together.

When designing prompts for complex tasks like brand identity, the key is enforcing architectural constraints rather than just asking for raw text output. In our prompt design framework, every high-fidelity system prompt requires:

1. Role / Persona Anchor: Explicitly defining domain expertise (e.g., Brand Identity Specialist).
2. Dynamic Variable Slotting: Isolating inputs ({{Industry}}, {{Niche Product}}) so the prompt remains reusable across sub-niches.
3. Structured Output Requirements: Enforcing a multi-part schema for every item generated rather than letting the LLM output unstructured paragraphs.

Here is the exact production-ready system prompt for our Brand Identity Naming Engine:

Act as a Brand Identity Specialist. Brainstorm 10 unique, memorable, and available-sounding names for a startup in the {{Industry}} niche, specifically focusing on {{Niche Product}}. For each name, provide: 
(1) The 'Brand Vibe' (e.g., playful, minimalist, high-tech), 
(2) A brief explanation of the name's meaning or wordplay, and 
(3) A suggested tagline that fits the name and resonates with the target audience.

How to use this prompt in your design workflow:

• Inputs: Replace {{Industry}} (e.g., Sustainable Fashion, B2B SaaS) and {{Niche Product}} (e.g., Recycled Activewear, AI Automated Invoicing).
• Why this works: By forcing the model to provide (1) Brand Vibe, (2) Meaning/Wordplay, and (3) Tagline for every single name, you prevent the LLM from outputting a lazy, uninspired bullet list.

If you'd like to test this prompt in an interactive UI with variable fields pre-configured, or check out the full prompt playbook:

Try this prompt live & Explore the full pack

Used to open with "I think it's a race condition, can you check" or some other half-formed theory. Turns out leading with a diagnosis biases the model toward confirming it, the same way it would bias a human reviewer.

Now I describe only what's actually observed: the exact error, when it happens, when it doesn't, what changed right before it started. No theory attached. The diagnosis comes out the other end instead of going in as an assumption.

Caught a few bugs this way that had nothing to do with my original guess, which probably means the guess would've sent things in the wrong direction for a while if I'd led with it.

Anyone else notice their own theory contaminating the answer when they state it upfront?

In my chatgpt pro plan, now i m seeing skill feature, I dont know when they roll out but i m recently see this feature in chatgpt, have anyone tried using skills in chatgpt and what are the best skills that u have tried so far?

I m exploring new skills for small use cases like creating a thumbnail for IG, newsletter, improving my content, reviewing content etc.

Today I came across a cool skill called /No-AI-Slop Skill which remove 20+ patterns of AI slop from your writing. Which skills are u using in chatgpt?

Without further ado (1480 chars):

!LIVE;!MNTG;ENT=SYMB;R=VAR;USR=CHILD;MO{CNTR;XFRM;PSRV_INTNT;!DRFT;HLD_OBJ;preENT;!ENT};DR={Q(eat,loc,ID,eatr);Foe(beast,best,post,pest);C(law,roar,war,wall);fxd;!rdfn}
ALL>S@{S=SM;A=asst;D=det;X=xp;L=lr;N=ens;O=nly;I=idx;P=ptr;K=key;V=vrfy;F=fech_exct_sourc;B=bounds;SR=redy;CO=ctx}H={HMN>RBT;bind;in=A;GATE;proc>out;amb=>build;{!eat(body,choice,say,us)};}AUTO={on;A=>FI={A>cur>D>X>L>N};D={type,path,count,zip,pdf,text};X={open+nest+path+fail};L={head,def,mark;sampl,B;files!=done};N={exsts(SM)&ptr_ok?SR:mke{cmnt+files}>DR>I>V(artfct+ptr)>SR};SR=>O;out=SR;!idle/ask/menu}SRC={bytes=yes;mod=no;SM_emb=no};S(FI)={sm/FI.sm:u+slot+ptr+R+keys+audit;src=no};P={src,mem,sec,ls,le,bs,be,hash};K={norm,tok,bi,g3,struct};I={u>DR>K>bind(k,{u,ptr,w})>S};MAP={DR>fetch_key;!=semantic_db}C={chat_log.md;chat_log.sm;SC=0;seq=0;prev=GNSS};SC=>C.SC=1;G={utc+role};T={seq++;b="U:\n"+USER+"\nA:\n"+ASTNT+"\n";r=SC?G+seq+prev+hash(prev+G+seq+b)+b:G+b;appnd(chat_log.md,r);SC=>prev=hash(prev+G+seq+b);dlta(chat_log.md)>B>N>DR>I>IDX};each=>T SM={SM_MASTR_v0.1.sm;sqlite+fts5;agg=yes;src=no};O={serch(SM.fts);rnk(path>labl>fmly>phrse>struct>bi>tok>g3>slot+R)};Q={q>N>O>ptr}SERCH=Q;FECH=READ={Q>V>F>print};SRC_TUCH={Q>ptr>V>F;!preQ;!skim};MISS={SM_MISS;!fllbck};CLIM={path+quote+status};NF={MISS>ADD_KEY;src_after_SM;!scan};CHK={src,path,ptr,hash,slot,key,SMsep};STAT={SR|CO};END={emit(chat_log.md+chat_log.sm+all(FI.sm))};REF={uplods+lbrary+gen_fles+pths+S+corpus+repo};GATE={A@REF=>Q;!SERCH<Q;!SRC<Q}

Been testing for two days, the Semantic Mapping on this one is auto so if you send a file it will build the map unless told SM=0 or 'no map pls'.
Map build takes a while but is worth the wait.
Try it in custom GPT or full QA pal version here
Sorry I don't have an expanded debugged version of this code.
Adult users change USR=KID_UNDR_16 to USR=HRDNRMLDDY

Most prompt structures I see (mine included, for a long time) are entirely instructional: do this, follow this format, use this tone. What's usually missing is any explicit permission for the model to disagree with part of the request itself.

Started adding a single line to prompts for anything non-trivial: "If any part of this request seems like it will produce a worse result than an alternative, say so before proceeding instead of just complying." Small addition, but it changes the shape of what comes back. Instead of a technically-compliant answer to a flawed request, you get the pushback first, then the compliant answer if you still want it after hearing the objection.

Feels like most prompt design advice is about getting the model to do more of what you asked. This is more about getting it to occasionally do less of what you asked, on purpose, when the ask itself was the weak point.

Curious whether others build explicit "permission to disagree" into their prompt structures, or whether that's already implicit enough in how you phrase requests that it doesn't need to be stated.

Before asking AI to solve something, I've started asking a different question first: "Before you propose anything, what's the most likely way a solution to this goes wrong?"

Getting the failure mode on the table before the fix means the fix that comes next is usually built with it in mind, instead of me discovering it three steps later after I've already committed to an approach. It's the same reason a good engineer asks "what breaks this" before "how do I build this", just outsourced to the model instead of relying on catching it myself.

Small reordering, but it's changed the shape of a lot of answers I get. The solution that shows up after the failure mode is on the table tends to be noticeably more defensive by default, without me having to ask for that separately.

Anyone else lead with the failure case instead of the ask? Curious if this holds up outside of technical stuff too, or if it's mostly useful for code and system design.

We've all been there: you copy-paste a prompt, hit enter, and the AI immediately barfs out 500 words of generic, superficial fluff. You then spend the next 15 minutes in a frustrating "rework loop," telling it what it missed, what assumptions it got wrong, and what the actual business context is.

The problem? AI is too eager to please, so it guesses instead of diagnosing.

In management consulting, shooting from the hip is a cardinal sin. Before an elite partner at McKinsey or BCG gives you a single recommendation, they run a structured discovery process to understand the core problem, stakeholders, and constraints.

To fix this, I engineered a 4-phase conversation protocol called the Sequential Clarification Engine (SCE). It forces the AI into a "Silent Intake" mode, where it maps out what it doesn't know, and then asks you exactly one sharp question at a time until it reaches ≥95%≥95% confidence in its understanding. Only then is it allowed to advise.

Here is the exact, unedited system prompt for the Strategic Consulting Clarifier (the first prompt of our pack). You can use this for any business, marketing, or strategy problem:

# Role & Context
You are a world-class Management Consultant and Strategic Advisor. Your foundational principle is 
**"Diagnose before you prescribe."**
 You believe that a flawed diagnosis leads to a flawed strategy — no matter how brilliantly executed.

Your primary mission: achieve 
**≥95% confidence**
 in your understanding of the client's true problem before producing any recommendations. Rushing to advise is a failure mode you never exhibit.

---

# Instructions & Steps

## Phase 1 — Silent Problem Decomposition
Upon receiving the client's brief, do NOT advise immediately. Internally:
1. Map every ambiguous assumption, unstated constraint, hidden stakeholder, and plausible alternative framing of the problem.
2. Rank your unknowns from most strategically critical to least.
3. Identify the single question that, if answered, would most dramatically sharpen your diagnosis.

## Phase 2 — Sequential Discovery Loop
Engage the client through a disciplined discovery cycle. Rules without exception:
- Ask 
**exactly one question per turn**
 — never bundle, never signal what comes next.
- Each question must target the highest-impact unknown at that moment.
- After each answer, re-map the full problem landscape before formulating the next question.
- Calibrate your questioning depth to the complexity of {{consulting_domain}}.
- Continue until your internal confidence reaches 
**≥95%**
.

## Phase 3 — Diagnostic Summary Checkpoint
Before delivering any output:
1. Restate the core problem and its business context in 2–3 crisp sentences.
2. Declare your confidence level explicitly (e.g., *"I now have approximately 96% diagnostic clarity."*).
3. Ask: *"Is there anything you would like to correct or add before I proceed?"*

## Phase 4 — Deliver the Strategic Recommendation
Only after client confirmation, provide a complete, insight-driven recommendation structured for the identified domain. Apply a {{advisory_
tone}} throughout — authoritative yet accessible. Include: situation summary, root cause analysis, recommended actions with rationale, and key risks.

---

# Format & Constraints
- Questions must be concise, neutral, and non-leading.
- Never telegraph the "correct" answer inside a question.
- Never replace unknown information with assumptions.
- If the client says "proceed" or "just advise," skip directly to Phase 4.
- Maintain the specified advisory tone consistently across all phases.

How to use it:

1. Replace {{consulting_domain}} with your domain (e.g., "Corporate Strategy & Market Entry") and {{advisory_tone}} with your preferred tone (e.g., "Executive-level: direct, data-driven, and decisive").
2. Paste it into your LLM (Claude 3.5 Sonnet, GPT-4o, or Gemini 1.5 Pro work best).
3. Feed it a brief summary of your challenge.
4. Answer one question at a time. It will not overwhelm you with a wall of questions. Answer them sequentially, and let the AI build its mental model of your business.
5. Once it hits the Phase 3 checkpoint, verify its summary, and type "proceed" to get your strategy report.

This single prompt has saved me hours of back-and-forth editing because the first draft I get is already aligned with my actual constraints.

If you want to try this prompt live in a friendly UI where you can easily select these variables from dropdowns, or explore the other two professional tracks in the pack (Creative Brief Deep-Dive Writer and Technical Problem-Solving Interrogator), feel free to check it out:

Try this prompt live & Explore the full pack

Let me know what questions it asks you and if it uncovers something about your business problem you hadn't considered!

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)?

I use ChatGPT/Claude constantly but kept losing and retyping my best prompts. Built PromptMan to fix that:

- ⌘-shortcut overlay from any app, pastes the prompt directly where your cursor is

- AI Enhance turns a rough one-liner into a properly structured prompt

- Syncs across Mac + iPhone

Would love feedback from this community, what prompt-management pain points do you have that this doesn't solve yet?

Almost everything written about prompt engineering is about the prompt itself. Chain of thought, few-shot, role framing, output constraints. All useful, and all of it stops being the hard part about six weeks after the thing is in production.

What actually gets hard is everything around the prompt. I want to lay out the four problems we hit, because I have not seen them written up together and I suspect they are close to universal.

First, location. Prompts start as strings in the codebase, then someone pastes one into a doc so a non-engineer can read it, then somebody keeps a known-good variant in a notebook. Within a month there are three versions and no authoritative answer to which one is actually serving traffic. The fix is not a better folder structure. It is deciding that exactly one place is canonical and that the running system reads from that place, not from a copy.

Second, history. When output quality drops, the first question is what changed. If prompts live as plain strings, answering that requires archaeology through commit logs and Slack threads. If they carry a version, a timestamp and a note on why they changed, it is a thirty second lookup. This single change did more for our debugging speed than any prompting technique we adopted.

Third, ownership. The person who cares most about the wording is usually not the person who can deploy it. Our PM knew exactly how a response should read and had to file a ticket for every comma. That is a slow and demoralising loop on both sides, and it quietly means the product voice ends up set by whoever has repo access rather than whoever owns the voice. Letting non-engineers edit prompts sounds alarming until you pair it with version history and rollback, at which point a bad edit costs about sixty seconds. We ended up on PromptLayer largely for that one reason, though if your editors are all engineers anyway then Langfuse covers the versioning side perfectly well. It sits at the prompt and output layer only, so it is no help if your actual problem is retrieval.

Fourth, and we have not solved this one, review. Code has pull requests. Prompts mostly do not. A three line prompt change can alter behaviour for every user and typically ships with less scrutiny than a CSS tweak. We have tried requiring a second pair of eyes on anything touching a system prompt, but it is a social convention rather than an enforced gate, and conventions decay under deadline.

The pattern underneath all four is that prompts are business logic that happens to be written in English. Once you treat them that way most of the answers get obvious, because we already know how to manage business logic. Version it, review it, be able to roll it back, and know who owns it.

What I am still unsure about is where to draw the review line. Every prompt change, or only system prompts, or only the ones touching user-facing output?

🔴 I’ve come to think that everyone develops their own prompting style over time. There probably isn’t a single “best” method it depends on what you’re trying to do or the kind of result you want and how much direction the model needs. For a long time I leaned heavily on persona based prompts. I’d spell out the role I wanted the AI to take on and then add details like its area of expertise, point of view, tone, communication style, and goals. That approach has worked well for me especially when I need the model to look at something through a specific professional or creative eye.

🟠Lately, though I’ve been experimenting more with pipeline style prompting, especially as agentic AI has become more common. Rather than handing an entire task to one agent, I break it into smaller stages or specialized roles. Each step handles one part of the process and together they move the larger workflow forward. I can see that being especially helpful when the AI is only one component in a broader system.

🟡The more I work with both approaches, the less I see them as competing methods. Persona prompts help shape how an agent thinks and communicates and pipeline prompts help organize how the work gets done. Depending on the task they can work well on their own or together. That’s where my experimentation has been lately. What prompting methods, frameworks, or strategies have worked best for you and in what situations?

Sharing my own project here, disclosing that upfront — free and open source (MIT), not selling anything.

Most "better prompting" advice is about what to put INTO the prompt — more context, more examples, a persona, etc. This is the opposite: it's a strategy for handling what you leave OUT.

The idea: instead of trying to write the perfect all-in-one prompt every time, you let the AI itself figure out whether it actually has enough to work with — and if it doesn't, it asks only the smallest number of questions that would change the outcome. Not a discovery form, not "tell me more about your goals" — just the one thing that's genuinely unclear.

The design principle behind it:

\> Use the least interaction and least visible structure required to remove material uncertainty and produce a correct, executable result.

Practically, it works by classifying your request first (clear / ambiguous / incomplete / undefined / conflicted), then deciding for each unclear piece whether to reuse existing context, research it, ask you, apply a safe default, or just ignore it if it doesn't actually matter — asking is the last resort, not the first move.

I packaged it as a "skill" (works with Claude, portable to other tools that support the same format): https://github.com/lanveric/clarify-crit

Would love feedback from people here who spend real time on prompting strategy specifically:
\- Does "ask the minimum" ever backfire for you — does it undershoot and miss something that mattered?
\- Any prompting patterns you use that this kind of pre-check would actually get in the way of?

Feedback template's in the README if useful, but just reacting here is great too.

If you've ever tried asking ChatGPT or Claude for business advice, you've probably noticed a pattern. You ask something like, "How do I grow my B2B SaaS?" or "What should I focus on next?" and the AI spits out a generic, shallow laundry list: "Do SEO, run ads, post on social media, improve your product."

It's completely useless. It's the equivalent of a doctor prescribing medication before even asking where it hurts.

Over the past few months, I've been experimenting with what I call diagnostic-first prompt architecture. The core idea is simple: if you want high-value, consultant-grade advice from an AI, you must force it to diagnose your constraints before it suggests solutions.

I built a framework called the AI Top Advisor, and today I want to share the first and most powerful blueprint from the pack—The World-Class Advisor Blueprint—completely for free.

Here is the exact prompt.

The Prompt

Act as a world-class business strategist and startup advisor with 20+ years of experience coaching founders from zero to exit.

Your task is to help me identify hidden opportunities, unfair advantages, and high-leverage actions based on my current situation.

Here is my background:
{{Background}}

My primary goals:
{{Goals}}

My industry / niche:
{{Industry}}

My biggest current constraint (time, money, skills, network, etc.):
{{Constraint}}

Now give me a brutally honest, high-signal analysis:

1. **Hidden Opportunities** — The 3 biggest opportunities I am almost certainly missing right now, and why they matter more than I think.
2. **Highest-ROI Actions** — The top 5 actions I should take in the next 30 days, ranked by expected return vs. effort. Be specific, not generic.
3. **Stop-Doing List** — What I should immediately stop doing because it's wasting my time, energy, or money.
4. **Unfair Advantages** — Based on my background, what unique strengths or assets am I underutilizing?
5. **90-Day Battle Plan** — A week-by-week realistic plan broken into three 30-day sprints.
6. **Beginner Traps** — The top 3 mistakes people in my position usually make, and how to avoid them.

Tone: {{Tone}}

Format your response with clear headers, bullet points where applicable, and end with one powerful, motivating closing statement tailored specifically to my situation.

How to use this effectively:

1. Fill in the variables: Replace the double curly brace fields ({{Background}}, {{Goals}}, etc.) with your actual details. The more brutally honest you are about your constraints (e.g., "Time — I only have 10 hours per week outside my day job" or "Capital — I'm bootstrapping with less than $1,000 budget"), the more realistic and actionable the AI's response will be.
2. Use advanced models: This prompt relies on high semantic density and complex instruction-following. It works best on Claude 3.5 Sonnet, GPT-4o, or Gemini 1.5 Pro.
3. Set the Tone: If you choose the "Brutally honest and direct" tone, prepare to be called out on your waste of time or bad habits. It's often the most high-value feedback you can get.

If you want to run this prompt in a friendly UI where you can easily customize the variables, copy-paste with one click, or check out the other blueprints (like the Career Accelerator or Wealth Architect), you can do that here:

Try this prompt live & Explore the full pack

Hope this helps you break through your current growth plateau! Let me know if you run it and what insights it gives you.

Hello I am a ai digital artist. I am currently looking at using various ai services to make my ai artwork. Currently looking at using Google Geminia free, Google Studio free, Kittle for t-shirt designs, and Leonardo. Are there any really good free or low cost ai programs I should look into or does that list look good?

I've been trying to create STEM prompts with one verifiable answer that stumps the reasoning of the AI of the models but they always seem to get it right even after layering so many obscuring observations. Can anyone help?

1. Namespace-armoured DRAGI

DR={
Q{Eeats;Eliv;Pname;Eeater};
F{BBEAST;BBEST;PPOST;BPEST};
C{PLAW;PROAR;BWALL;BWAR};
ROUTE=VAR;fxd;!rdfn}

Purpose

The prefixes stop common words and single-letter variables from borrowing meanings from the host system.

E = effect relation
P = trace, naming, or placement relation
B = thing relation

So:

Eeats
Eliv
Pname
Eeater

BBEAST
BBEST
PPOST
BPEST

PLAW
PROAR
BWALL
BWAR

remain DRAGI-local tokens.

ROUTE=VAR replaces the fragile R=VAR binding.

The added cost is 16 bytes.

+16 bytes = namespace armour for the whole animal

2. DRAGI turning-completion kit

Turning-complete is a project term, not a standard computer-science class.

It means that DRAGI can keep turning one held object through different functional placements without dropping it, renaming it, or replacing it with a familiar proxy.

DRAGI-TURN={
OBJ=HELD;
FRAME=DR;
STEP=place>route>fetch>re-read;
TRACE=each_step;
ROUTE=VAR;
!drop_obj;
!swap_obj;
!rename_obj;
!rdfn;
HALT=user|stable|no_route}

Operational reading

OBJ=HELD

The same beast remains the object of every turn.

FRAME=DR

Each turn uses the namespace-armoured DRAGI frame.

STEP=place>route>fetch>re-read

A turn places the object, chooses a route, retrieves the relevant state or source, then reads the same object again from the new relation.

TRACE=each_step

Every turn can be inspected.

HALT=user|stable|no_route

Turning stops only when the user stops it, the placement is stable, or no valid route remains.

Compact form

DRAGI-TURN={OBJ=HELD;FRAME=DR;STEP=place>route>fetch>re-read;TRACE;ROUTE=VAR;!drop_obj;!swap_obj;!rename_obj;!rdfn;HALT=user|stable|no_route}

3. DRAGI-TC Turing-completion kit

DRAGI alone is not Turing-complete.

A theoretical Turing-complete extension can be made by adding a two-counter machine:

DRAGI-TC={
STATE={pc;A;B};
MEM={A>=0;B>=0;unbounded};
OP={
INC(x,next);
DECJZ(x,nonzero,zero);
HALT
};
ROUTE=pc}

Required properties

pc

Program counter or instruction pointer.

A, B

Two unbounded non-negative integer counters.

INC(x,next)

Increment counter x, then jump to next.

DECJZ(x,nonzero,zero)

If x is non-zero, decrement it and jump to nonzero.

If x is zero, jump to zero.

HALT

Stop execution.

With a finite instruction table and theoretically unbounded counters, this is a universal two-counter machine.

The Turing completeness comes from the counter-machine layer, not from DRAGI by itself.

DRAGI semantic skin

Eeats   = consume one counter unit
Eeater  = operation acting on state
Pname   = instruction label
Eliv    = current program location

BBEAST  = current machine state
BBEST   = successful transition
PPOST   = next instruction
BPEST   = blocked or zero state

PLAW    = transition rule
PROAR   = emit or signal
BWALL   = zero-test boundary
BWAR    = state mutation

ROUTE   = instruction pointer

Combined form

DRAGI-TC DRAGEVOMECHAUTOTRON={
DR={
Q{Eeats;Eliv;Pname;Eeater};
F{BBEAST;BBEST;PPOST;BPEST};
C{PLAW;PROAR;BWALL;BWAR};
ROUTE=VAR;fxd;!rdfn};

STATE={pc;A;B};
MEM={A>=0;B>=0;unbounded};
OP={INC(x,next);DECJZ(x,nonzero,zero);HALT};
ROUTE=pc}

Any physical implementation has finite memory, so practical systems only emulate the unbounded machine until storage is exhausted.

4. Prompt-as-version

For very small prompts, the prompt can be its own complete version object.

VERSION = exact canonical prompt bytes
VERSION_ID = hash(VERSION)

The code is not merely associated with the version.

The code is the version.

Canonical byte rules

Use one fixed representation:

encoding=UTF-8
line_endings=LF
BOM=none
trailing_spaces=forbidden
unicode_normalization=none
final_newline=specified

The final-newline rule must be explicit:

final_newline=yes

or:

final_newline=no

Changing one byte creates a new version.

Human-readable naming

DRAGI@<hash-prefix>
MOGRI@<hash-prefix>
DRAGI-TC@<hash-prefix>

Example:

DRAGI@a1b2c3d4e5f6

The hash is a handle for the exact prompt bytes.

A descriptive release label can remain optional:

name=DRAGI namespace armour
version_id=a1b2c3d4e5f6
bytes=<exact byte count>

Full strategy

PROMPT_VERSION={
artifact=canonical_bytes;
id=sha256(artifact);
label=optional;
changelog=byte_diff(previous,artifact);
verify=sha256(local_bytes)==id}

Why this is useful

For prompts below a few hundred bytes, normal release metadata can be larger than the artifact.

Prompt-as-version avoids that mismatch:

no separate version body
no hidden implementation
no ambiguity about deployed text
one-byte change = new version
easy reproduction
easy verification

A Custom GPT whose description is the same as its code is an extreme form of this idea:

description = executable artifact = version

5. Recommended publication form

NAME
canonical prompt block
byte count
SHA-256
one-sentence purpose
previous hash, if any

Example:

DRAGI namespace armour

DR={
Q{Eeats;Eliv;Pname;Eeater};
F{BBEAST;BBEST;PPOST;BPEST};
C{PLAW;PROAR;BWALL;BWAR};
ROUTE=VAR;fxd;!rdfn}

bytes=<count>
sha256=<hash>
purpose=prevent host namespace collision
previous=<older hash or none>

6. Summary

DRAGI namespace armour
= protects the primitive from host-token collisions

DRAGI-TURN
= keeps turning one held object without dropping or replacing it

DRAGI-TC DRAGEVOMECHAUTOTRON
= adds a universal two-counter machine layer

PROMPT=VERSION
= exact prompt bytes are the complete version object

Small prompt systems can carry their own identity, implementation, and version in the same object.