PROMPTS
1346 original and curated AI prompts across image, video, music, and tutorials. Browse by tool, use case, and difficulty; each entry shows its available notes and examples.
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Prompt Chaining: Split a Big Task into Three Steps for Better Quality
Don't feed an entire complex task into a single prompt — split it into three separate steps ('decompose → produce each part → integrate'), and the output is noticeably more stable and complete.
Self-Critique Revision Loop: Draft, Self-Criticize, Revise — Three Rounds of Polish in One Prompt
Has the AI produce a first draft, then switch into a strict editor role to pick apart its own flaws, and finally rewrite based on that critique — effectively running three rounds of polish inside a single prompt.
Rubric-Driven Output: Tell AI What a Perfect Score Looks Like First
Write out the grading rubric in your head and give it to the AI first — it will use that yardstick to self-align, self-evaluate, and fix whatever falls short, getting you much closer to the quality you actually want.
The Anti-Goal Sandwich: One Line to Stop AI's Most Common Mistake
Explicitly naming "the most common failure mode AI makes on this type of task" in the prompt and telling it to proactively avoid that mode — 2026 field tests show roughly a 28-30% reduction in errors.
XML Tag Structuring: 28% Fewer Parsing Errors Than Markdown
Wraps instructions, data, rules, and schemas in XML tags when feeding them to an AI — tested to produce roughly 28% fewer errors on structured-extraction tasks than using Markdown headers.
Contrastive Example Calibration: Show It What You Want AND What You Don't — One Negative Example Beats Three Positive Ones
Demonstrate both the 'desired output' and the 'output that shouldn't appear' at the same time, so the AI calibrates between the two poles; for tasks about tone and precision, a single negative example often outperforms three positive ones.
Chain-of-Thought Scaffold: A Fixed 5-Step "Think Before You Answer" Process
Forces the AI to think through 5 fixed steps before answering — restate → list assumptions → reason step by step → self-check → conclude. Field tests show roughly a 30% reduction in confidently wrong answers on complex reasoning tasks.
5 Claude Repeat-Task Templates That Lasted 6+ Months — and the 3 Retention Rules
After 18 months of testing 60 Claude workflows, the retention pattern: annoying but not painful, output has a destination, input under 30 seconds. The 5 templates that survived 6+ months (Friday review / meeting follow-up / client weekly report / Monday briefing / pipeline update).
5 Prompt Engineering Techniques That Actually Move the Needle (XML Beats Markdown by 28%)
Five quantifiable techniques from an AI engineer's hundreds of hours testing across GPT-4, Claude, and Gemini: CoT scaffolding, persona + goal + anti-goal, XML being 28% more accurate than markdown, negative examples outperforming positive ones, and prompt chaining beating the mega-prompt.
Turn Off ChatGPT's Polite Lying — 3 Anti-Sycophancy Instructions
By default, helpful = agreeable = burying the real problem in paragraph four. Three one-line instructions reverse it: problem first, force a recommendation, explicitly forbid softening. The difference between reading and fixing.
A Red Teamer's View: 3 High-Capability Prompt Patterns That Work Across Models
Three patterns shared by the HackAPrompt 2.0 champion: frame the task as an audit, make the abstraction level explicit, and stage the context like production. They work on Claude, GPT, and Gemini alike.
AI Agent Harness Engineering: Separating the LLM from the Deterministic Orchestration Layer
Mitchell Hashimoto's "Agent = Model + Harness" framework. The reason 88% of enterprise AI agents never reach prod isn't the prompt — it's the missing deterministic orchestration layer. Four CS primitives you can't skip: state machine, idempotency, DAG, DLQ.
COMMAND PLANNING MODE: A 9-Section Structured Planning Prompt
No conversation, no suggestions, no attempts — it just turns any situation into a structured execution plan. Fixed 9-block output: Reality / Objective / 4-phase Timeline / Tactics / Threats / Defenses / Contingency / Leverage / Confidence. Same input always yields the same output.
System vuln scanner: analyze your architecture, find attack chains, estimate time-to-compromise
More than "list the CVEs." It finds how individual weaknesses chain into an attack and estimates how long an AI-augmented attacker would need to break in. A take on the OpenAI Daybreak concept, built for small teams. Feed in a stack description (Next.js + Supabase + Clerk, etc.) and get back critical paths plus a patch-priority order.
ChatGPT Custom Instructions: describe the relationship, not your resume
Most people fill Custom Instructions with a LinkedIn resume, "I'm a software engineer, I like bullet points," and it does nothing. Swap in a description of the conversational relationship you want, and three sentences will completely change how the model behaves.
reClaim: Turn a Frontier Model Into a Research Agent With Confidence Scores
Scores every claim on three axes (A: sources / B: counterevidence / C: completeness), plus a 4-tier source hierarchy, adversarial self-poking, and 4 output modes.
AI attack-surface audit: see yourself the way an AI-augmented attacker does
Written after Google's threat-intelligence report in May 2026. AI hacking has gone from nascent to industrial scale, with commercial AI models digging up zero-days humans had missed for decades. This prompt lays out every place you can be attacked.
Job-search strategy: turn ChatGPT into a 4-in-1 (auditor + analyst + advisor)
Combines a resume auditor, market analyst, system auditor, and strategist. It won't flatter you, won't over-reassure you, and won't push you toward roles that don't fit. Includes a 10-step process and ghost-job detection.
English copy editor: 4 priorities, emotional resonance, and structural flow
More than a grammar fix. Purpose-built for persuasive and narrative writing: four priorities (clarity → tone → emotional resonance → structure), an explanation for every change, plus extra suggestions.
A Universal Decision Prompt: Use "What Will You Regret in a Year" to Force a Verdict
Stop picking a role for every decision (financial planner / career coach / relationship coach) — one universal prompt, four steps. The regret line is the core — it forces the model from "it depends" to "actually do this."
CV Writer Ruleset: It Interviews You STAR-Style and Bans "Dynamic Team Player"
The pain of writing a CV isn't the layout — it's listing duties instead of achievements, then having AI pile on and turn you into a "dynamic team player." This ruleset forbids AI from drafting anything until it has your evidence and positioning, and walks you through one question at a time.
Voice Memo → Tomorrow's Priorities (With a Confirmation Round)
Before you log off, ramble into your phone mic for 2 minutes, hand it to Claude, and get back a sorted list: first thing tomorrow morning + ranked to-dos + waiting-on + can-defer. Auto-detects daily vs. weekly mode.
The 60-Second Pre-Send Check: Catch the Message You'll Regret Tomorrow Morning
The author wrote this after 30% of their heated replies made them wince the next day — costing a few deals and a friendship. The trick: don't let AI rewrite it, let it show you what the calm version would look like. Once you see it, you usually can't go back to the original.
Grading essays as a teacher: a 3-step chain (lens → diagnose → tone)
A method dreamed up by a teacher 19 essays into a 30-essay all-nighter. Where typical AI prompts "act as an English teacher" and devolve into a wall of rubric noise, this chain solves the real grading workflow: don't comment on everything.