Precision Prompting: Force Superior AI Output with Constraints

Описание: #promptengineering #jailbreak #chatgpt

The Precision Prompting Manual: Forcing Superior AI Output through Constraints

Introduction: Beyond Generic Responses
Generic AI answers stem from unconstrained prompts allowing safe, vague paths. To get sharp, specific outputs, apply constraints that invalidate generics, forcing trade-offs, mechanisms, diagnostics, and defensible decisions. This guide covers foundational, tactical, and advanced patterns for practical use.

Foundational Constraints: Forcing Choice and Verifiability
Eliminate "it depends" by compelling clear stances.

Commitment Requirements:** Force a single choice, defense against counters, and opportunity costs.

How: Demand one option, justify vs. alternatives, state losses.

Example (Phone Upgrade): "Recommend one phone model under $800, defend against cheaper options, explain what I sacrifice."

Why: Makes neutrality impossible, yields real decision support.

Trade-Off Constraint: Admit goal conflicts and sacrifices.

How: Prioritize one goal, detail concessions.

Example (Health): "Prioritize muscle gain over enjoyment; list sacrifices like diet restrictions."

Why: Prevents all-benefits pretense, delivers realistic advice.

Failure Modes with Diagnostics: Map failures to symptoms and tests.

How: List distinct modes, unique signs, simple checks.

Example (Car Start): "List failure modes (battery, starter), symptoms (no lights vs. clicking), DIY tests."

Why: Turns lists into actionable troubleshooting.

Tactical Playbook: Deeper Divergence
Alter incentives, tools, structures for insightful paths.

Specific Lens with Incentives:** Use accountable roles for defensible answers.

How: Assign high-stakes persona (e.g., lawyer).

Example (Job Change): "As a career coach facing lawsuits for bad advice, recommend staying or quitting."

Why: Shifts from comfort to scrutiny, narrows claims.

Toolbox Restriction: Limit to one framework for depth.

How: Enforce a single lens, output its elements (e.g., incentives).

Example (Negotiation): "Analyze via game theory: payoffs, signals."

Why: Avoids mushy blends, forces precise translation.

Uncommon Structural Constraints: Mandate real-world formats.

How: Use memo, postmortem; ban bullets.

Example (Quitting Job): "Write as letter from future self, reflecting on decision."

Why: Disrupts defaults, enhances coherence.

Concrete Context Limitations: Add specifics to tailor strategies.

How: Include limits (budget, red lines).

Example (Salary): "Negotiate from $72k; no quitting threats, tight company budget."

Why: Starves ambiguity, yields situational plans.

Advanced Techniques: Restructuring Generation

Manipulate critique, words, reasoning for robust outputs.

Adversarial Prompt Chaining: Internal debate for refinement.

How: Sequence roles: propose, critique, defend.

Example (Purchase): "Propose buying; critique as skeptic; refine."

Why: Exposes assumptions, strengthens ideas.

Probability Inversion: Ban buzzwords for originality.

How: Forbid generics; use lived experiences.

Example (Productivity): "Describe habits without 'optimize' or 'focus'; use friction, anxiety."

Why: Escapes clichés, maps to real life.

Synthetic Context Override: Rule out commons for subtlety.

How: State facts, forbid usual causes.

Example (Breakup): "Not communication or cheating; suggest other causes."

Why: Explores complex dynamics.

Scoring Rules: Gamify for quantified specificity.

How: Penalize generics, reward risks; score and recommend.

Example (Car Buy): "-10 for vagueness, +5 per quantified risk; decide based on score."

Why: Drives data-driven commitments.

Conclusion: Guiding Principle
Redesign prompts to invalidate generics: force choices, mechanisms, diagnostics, penalties for vagueness. Elevate from averages to decision support.

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