1328 hand-tested AI prompts across image, video, and music — each with full context, variables, and a sample output. Content is authored in Traditional Chinese; the UI here is English.
Feed the AI structured background, what's already been tried, constraints, and available tools/data structures before giving it the task — in 2026 the real leverage point has shifted from 'prompt tricks' to 'context.'
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.
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.
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.
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.
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.
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.
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.
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).
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.
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.
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.
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.
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.
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.
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.
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.
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.
This isn't "adding sound effects" — it's changing the performance context. When Suno sees [Live Crowd] or *crowd cheering*, it dials back vocal processing, loosens the timing, and adds spatial depth. It genuinely sounds like a live concert recording.
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.
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.
Why does Suno always sound generic? Because you only describe "genre + vibe." Swap in "Jupiter-8 saw wave bass with reverb tail" and Suno gives you the exact sound you're after.
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."
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.