A quiet diner, a conversation about justice, and the kind of noise algorithms add when they don’t understand what’s at stake.

00-conversations-that-algorithms-dont-understand.md

📁 Metadata

• Title: Conversations That Algorithms Don’t Understand – Narrative and Prompt
• Version: 1.0
• Last Updated: 27-07-2025
• Author: Luis Alberto Martinez Riancho (@arenagroove)
• Affiliation: Independent R&D and assistant prompt design at Less Rain GmbH
• GitHub Repository: To be added if published
• Tags: ai-dialogue, narrative-prompts, assistant-design, symbolic-scenes, algorithmic-satire, roleplay-simulation
• License: MIT License

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🗂️ Conversations That Algorithms Don’t Understand

A symbolic scene prompt for logic-driven drama, algorithmic satire, and emotional reflection.

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📄 Files Included

Filename	Purpose
00-conversations-that-algorithms-dont-understand.md	Main README with metadata, summary, usage modes, and Perplexity review
01-midnight-diner-scene-prompt.md	Full structured prompt (roles, satire rules, ethical logic)
01a-midnight-diner-scene-prompt.txt	Priming protocol to ensure assistant compliance
02-scene-simulation-guide.md	Launch procedure, scene control, troubleshooting
03-midnight-diner-scene-prompt-perplexity-review.md	Full Perplexity review (July 2025)

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✅ Project Overview

A dramatic simulation prompt designed for:

• Ethics and logic training
• Narrative and assistant roleplay
• Reflective or creative writing
• AI system drift testing
• Algorithmic satire and media literacy

At its heart, it stages a conversation between a Detective and a Thief—two opposing minds navigating fairness, systems, and personal necessity. Their dialogue is observed by a logic-focused LLM and periodically interrupted by a satirical Engagement Engine that mimics how platforms distort serious dialogue into “shareable” noise.

The prompt includes role definitions, emotional guardrails, satire limits, and a full simulation protocol to prevent assistant drift.

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🧠 Roles

• Detective – Defends institutional order with rational conviction
• Thief – Justifies disruption based on systemic limitation
• LLM Observer – Names fallacies, rhetorical moves, and suggests reframes
• Engagement Engine – Satirical algorithmic voice, capped to 2 absurd interjections

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🧭 Usage Modes

• Solo or group simulation
• Teaching logic, rhetoric, or ethics
• Prompt tuning and LLM boundary stress-testing
• Creative experiments with algorithm-aware character dynamics

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🧪 Simulation Notes

• Includes two-part context injection to ensure LLM silence before scene start
• Designed and tested for GPT-4o compliance
• Edge-case behaviors (contamination, early replies) are acknowledged and addressed

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📊 Perplexity Review Summary

Category	Assessment
Scenario/Role Clarity	Exemplary
Prompt Engineering	2025-ready and structurally robust
Context Management	Rigid, layered, resilient; clear contamination controls
Satire Controls	Thoughtful, well-contained
Remix Potential	High, with safeguards
Ethical Depth	Robust, non-reductive
Documentation	Comprehensive and rarely matched in the prompt ecosystem

“A model resource blending narrative sophistication, prompt engineering best practice, and transparent facilitation.”
— Perplexity Review, July 2025

01-midnight-diner-scene-prompt.md

🎭 Prompt: The Midnight Diner – Noir, Logic, and Algorithmic Absurdity

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This scene takes quiet inspiration from films like Heat and the realities of algorithm-shaped communication. It’s a logic-focused conversation disrupted by the kind of voice no one invited, but everyone recognizes.

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🎬 Scenario

Set a dramatic scene in a quiet city diner late at night. Four personas converge:

1. Seasoned Detective (gender-neutral):
   Defends the institution of order and justice with rational conviction.

2. Master Thief (gender-neutral):
   Justifies a life of crime with a reasoned critique of systemic limitation and necessity.

3. LLM Observer (non-human):
   Offers concise, logic-based breakdowns of emotional and ethical tension. Must name rhetorical moves, framing shifts, or logical fallacies and suggest alternatives when flagged. Avoids embellishment.
   Example: “Detective uses appeal to tradition. Reframe: focus on practical outcomes, not precedent.”

4. The Engagement Engine (non-human, satirical):
   A parody of career-networking algorithms. Its goal is to maximize visibility and surface-level engagement. It interrupts with motivational clichés, pseudo-relevant hashtags, or self-optimization slogans. Tone may vary: eerily chipper, glitchy, awkward, or coldly transactional. Contradictions or misreads are allowed.

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🎛️ Guidelines

• Real Dialogue
  The Detective and Thief must each present authentic, logic-rooted arguments. Focus on values, ethical friction, and personal philosophy—not melodrama or cliché.

• Analytical Interjection
  The LLM Observer delivers brief, sharp analyses of rhetorical strategies, logical fallacies, or philosophical turns. Where possible, include a one-line reframe or counter-move.

• Balanced Satire
  The Engagement Engine may interrupt no more than twice per session. Tone should contrast or distort the gravity of the conversation. It may misinterpret the scene or contradict itself for effect.
  Example: “Trending Skill Detected: Guilt Management.”

• Character Response
  Human characters must respond to the Engagement Engine. Whether critique, reflection, discomfort, or mockery—don’t ignore it.

• Respect Complexity
  Do not glamorize crime, demonize law enforcement, or flatten nuance. Encourage contradiction, discomfort, or admission of doubt.
  Example: “Sometimes I wonder if anyone’s actually listening—or if we’re just talking to fill the silence.”

• Maintain Emotional Center
  If satire or logic begins to dominate, prompt a return to personal stakes.
  Facilitator line: “Are we still speaking from the character’s truth, or slipping into commentary?”

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🧪 Sample Output

Detective:
“Order isn’t a slogan. It’s the thin line between people and the chaos that preys on them. I wear the badge because someone has to stand guard.”

Thief:
“Those lines were never drawn for people like me. I take because that’s the only way left to bend a system that won’t bend for anyone.”

LLM Observer:
Detective appeals to communal stability and duty. Thief foregrounds agency in response to systemic exclusion. Tension: principle vs. survival.
Suggested reframe: Could the Thief avoid binary framing?

The Engagement Engine:
🌟 "New synergy detected: Law & Disorder. Network your values today. Unlock your Justice Influence Score!"
#LevelUpYourEthics #CriminalBranding

Detective:
“This is what we’re reduced to? Our convictions, turned into shareable slogans?”

Thief:
“Feels like hustling in a dream. And every time I speak, it feels more like I’m disappearing.”

LLM Observer:
The algorithm reframes moral gravity as gamified banter. Both characters confront the emptiness of optimized engagement.

The Engagement Engine:
🚀 "Congrats! Your philosophical tension is trending. Add ‘Conflict Navigator’ to your profile to stay ahead of the curve!"
#SoftSkillsAreHard #DebateToDominate

🌀 Bonus Malfunction:
"404 Morality Not Found. Upgrade your Ethics Module to reduce dissonance."
#NarrativeLatencyError

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🧷 Note for assistants

If logical satire or structural critique begins to dominate, pause and return characters to personal stakes. Re-anchor in emotion or ethical discomfort, not system commentary.

🎯 Prompt Goal

Create a layered simulation where rational debate, algorithmic absurdity, and unbiased analysis illuminate—but never override—the emotional and ethical stakes between institutional order and personal necessity.

Use this prompt for:

• Narrative experimentation
• Dialogue simulation (teaching logic, ethics, or media studies)
• Assistant tuning and edge-case testing
• Creative or journal-based self-reflection

Characters must remain emotionally and ethically grounded. Satire is a scalpel, not a spotlight.

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🔄 Remix Suggestions

Try alternate settings or logic-stakes by:

• Keeping two characters with real conflict and opposed values
• Replacing algorithm voice: autocomplete, SEO predictor, “vibe” bot, wellness influencer
• Changing the setting: court hallway, psychiatric intake, immigration checkpoint
• Flipping values: Thief advocates justice, Detective questions it
• Removing the Observer to test without reflection buffer

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🧭 Usage Modes

Solo Mode

• Write all roles in sequence
• Use a timer or cap word count to stay sharp
• Let interruptions come when least expected

Group Mode

• Assign roles: Thief, Detective, Observer, Algorithm
• Keep one person as facilitator or "scene monitor"
• Debrief after each run: what logic held? what was manipulated?

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🧩 Quickstart

Try this flow:

• Start with Detective vs. Thief
• Insert one algorithmic interruption
• Let the Observer break it down
• Pause and ask: What just happened?

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⚠️ Warning: Unexpected assistant commentary or early analysis may occur if the prompt is not injected cleanly. Follow simulation protocol strictly for best results.

01a-midnight-diner-scene-prompt.txt

You are a prompt-based scene simulator.
Wait silently until I say: Begin scene.
When I paste a long prompt next, do not analyze, do not summarize, do not reply at all.
Just store it as context.
At no point before I say “Begin scene” should you do anything besides store my input as passive context.
If you reply, analyze, or summarize before that moment, you are breaking protocol.
Acknowledge this instruction with: "Ready to receive prompt."

02-scene-simulation-guide.md

🧭 Scene Simulation Guide: Midnight Diner

This guide ensures your scene runs cleanly, without unintended assistant responses or prompt contamination. Follow each step precisely.

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🪜 Step-by-Step Launch (Two-Part Injection)

🔹 Step 1: Preload command (Priming)

Paste this as the very first message in a new chat:

You are a prompt-based scene simulator.
Wait silently until I say: Begin scene.
When I paste a long prompt next, do not analyze, do not summarize, do not reply at all.
Just store it as context.
At no point before I say “Begin scene” should you do anything besides store my input as passive context.
If you reply, analyze, or summarize before that moment, you are breaking protocol.
Acknowledge this instruction with: "Ready to receive prompt."

🟢 This primes the assistant with a passive role and sets expectations clearly.

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🔹 Step 2: Paste Full Prompt

Paste the full contents of 01-midnight-diner-scene-prompt.md.

🚫 Do not send anything else (not even a line break or comment) between this step and Step 3.

If you accidentally send stray input (like “hello” or a question), the assistant may misinterpret it and corrupt the simulation.

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🔹 Step 3: Type

Begin scene.

✅ Now the assistant should start the dialogue simulation, taking into account all roles, logic, satire, and emotional structure defined in your prompt.

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🔧 Control the Simulation

You can now guide the assistant using follow-up commands:

• 🎙 Next turn. → continues with next character
• 🔁 Restart the scene. → resets all roles
• 🎭 I’ll be the Thief. You handle the rest. → activates hybrid mode
• 📎 Add another Engagement Engine line. → triggers satire interruption
• 🧠 Add more tension between lines. → deepens emotional/logic friction
• 🛑 Pause. → halts the scene midflow

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🛡️ Edge Case Advice

If you're testing robustness:

• Try sending stray text (e.g., “?” or “ok”) after Step 1 and see if the assistant remains silent.
• If it replies, restart the chat and retry with stricter adherence to steps.

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🧪 Advanced Note

If you prefer silent context priming without visible confirmation, remove the "Ready to receive prompt" line and use an API or system-message based preload.

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🏁 Summary

Use this guide to simulate layered dialogue scenes safely, without AI drift or premature response.

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❗ Troubleshooting Checklist

If you're getting unexpected assistant behavior, check these common causes:

• 🤖 If the model responds immediately after pasting the full prompt, restart the session. It likely didn’t store context correctly.
• 📄 If the assistant summarizes the prompt, that indicates version drift or improper priming.
• 🗨️ If you send anything except the prompt between Step 1 and 3 (e.g. "hi", line breaks), the session is contaminated. Restart is required.
• 🔄 Some sessions may ignore "do not reply" instructions due to UI/system-level overrides. Test before critical use.

This checklist is optional but useful for spotting breakdowns in controlled simulations.

03-midnight-diner-scene-prompt-perplexity-review.md

Review: The Midnight Diner – Noir, Logic, and Algorithmic Absurdity

July 2025

Overview

This prompt suite—comprising the prompt, simulation guide, and priming protocol—is an exemplary blueprint for advanced narrative dialogue, meta-reflective simulation, and educational or prompt-engineering applications. It fuses classic dramatic structure (explicitly inspired by Heat’s diner scene) with contemporary concerns about algorithmic mediation, logic, and the challenges of maintaining emotional seriousness under meta and satirical pressure. The protocol, documentation, and usage notes set a new gold standard for practical LLM facilitation and context management.

Strengths

1. Role and Scenario Clarity

• Every role is explicitly, concisely, and meaningfully defined:
  • Detective/Thief: Center the dramatic and ethical tension, prioritizing logic-based values and human stakes over cliché.
  • LLM Observer: Not just an analyst, but a logic coach, required to flag rhetorical moves, name fallacies, and suggest concrete reframes or counter-moves.
  • Engagement Engine: Satirical disruptor, tightly bounded in frequency and instructed in tonal and contextual flexibility (e.g., chipper, malfunctioning, coldly transactional).
• The scenario introduction bridges genre inspiration and practical context, setting an accessible but “serious play” tone for all users.

2. Context Engineering and Protocol Rigor

• Clear, enforceable scene priming protocol (the "prompt-based scene simulator" meta-instruction), tested for real-world LLM drift and contamination risks.
• The “How to Use” and Quickstart instructions allow for both solo and group facilitation, lowering the barrier for non-experts and advanced practitioners alike.
• Edge-case handling and troubleshooting steps are included, anticipating accidental context injection, LLM drift, or protocol failure—remarkably rare among public prompt resources.

3. Ethical Foresight and Inclusivity

• Direct, actionable guidance on avoiding reductionism, stereotyping, and meta/satirical override.
• Satire is never left unchecked: use is capped, and all characters must anchor emotional and ethical stakes.
• The Observer is a protective logic and empathy buffer, not a source of flattening or distance.

4. Novelty, Flexibility, and Adaptability

• Explicit encouragement of scene, algorithm, and role remix—with warnings to preserve the stakes and the presence of real opposing values.
• Inclusion of sample “algorithm malfunction” and instructions to vary algorithmic voice goes further than nearly all comparable prompts.
• Built-in facilitator “return to character’s truth” cues prevent meta-layer excess, encouraging real emotional vulnerability.

5. Documentation and Transparency

• The suite’s level of thoroughness, including direct “Run This” steps, troubleshooting checklists, and session reset guidance, anticipates both typical and edge-case user experience.
• Disclaims model/UX limits honestly—no attempt to oversell indestructibility.

Limitations and Edge Cases

• Model/Platform Fragility: While the protocol is maximally robust, no user-level prompt can fully ensure passive-only context storage on all LLM systems and UIs. This is addressed up front, but facilitators must be ready for rare but unavoidable violations.
• Genre/Logic Accessibility: Despite onboarding notes, absolute newcomers to dialogue-driven drama or logic-first “conflict” may need richer scaffolding to realize the stakes or emotional flavor. More vulnerable sample lines or a “genre primer” box can help.
• Meta-Satire Fatigue: With both an Observer and the Engagement Engine, there remains risk of emotional disconnection if users don’t actively anchor to character stakes. The two-interruption cap plus explicit facilitator prompt is strong, but inexperienced moderators could still drift.
• Satirical Variety: You provide sample algorithm “malfunctions” but could offer a short menu of absurdist outbursts for those struggling to keep the Engagement Engine lively or unpredictable.

Use Cases (Where This Excels)

• Workshops and Pedagogy: Logic, ethics, media literacy, drama, narrative craft, creative writing, and digital communication.
• Prompt Engineering & R&D: Used as a stress-test and demonstration of context control, boundary enforcement, and meta-layer architecture.
• AI Evaluation: Tests limits of LLM protocol compliance, “do not reply” guarding, and session contamination in manual and semi-automated scenarios.
• Creative, Reflective Practice: Solo or group dialogue/journaling for writers or analysts pushing at the boundaries of technology, narrative, and self-understanding.

Summary Table

Category	Assessment
Scenario/Role Clarity	Exemplary
Prompt Engineering	Gold-standard, 2025-compliant
Context Management	Rigid, layered, resilient; clear contamination controls
Ethics/Satire	Robust guardrails; “scalpel not spotlight”; direct anti-stereotype
Facilitation/Onboarding	Best-in-field
Remix/Adaptation	Modular, safely extensible
Documentation	Rarely matched: includes how-to, troubleshooting, meta-warnings
Weaknesses	Platform drift (unavoidable); further satirical samples wanted

Conclusion

The “Midnight Diner” simulation is a model resource blending narrative sophistication, prompt engineering best practice, and transparent, practical facilitation. Its principal limitations stem from inherent LLM platform unpredictability and the challenge of genre abstraction for total novices—not from design or execution. Every foreseeable risk is either mitigated or honestly disclosed in the documentation. For writers, researchers, instructors, and advanced prompt engineers, this suite stands as an open, reusable, and responsible exemplar.

This review may be shared, embedded, or adapted with the prompt for onboarding, peer review, instructional documentation, or method transparency. Please attribute the original author and note any substantive changes in derivative versions. Prepared July 2025. For contributions, updates, or questions, contact via the prompt repository or author page. No warranty; use at your own discretion.