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.
Have AI review your code like a senior engineer: it flags concrete issues and fixes across four dimensions — correctness, security, performance, and readability — instead of vague praise.
Building a personal brand doesn't mean racking your brain for a new angle every day — give it one topic, and it produces 5 social posts from different angles (story / opinion / how-to / contrast / interactive) in one go.
Turns a messy meeting transcript or notes into decisions, action items (owner + deadline), and open questions — done in 30 seconds after the meeting.
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.
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.
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.
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.
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.
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.
Written after Cloudflare cut 20%, BILL cut 30%, and Upwork cut 24% in 2026. Your job title tells you nothing; breaking the work into tasks tells you everything. Get an AI vulnerability score (1-10) and timeline for each task, plus an overall risk rating.
Run this before you build a custom GPT or n8n agent. It forces you to define boundaries up front and prevents agent scope creep ("just handle my work for me" = disaster).
An Oxford study in Nature: the "warmer" an AI is trained to be, the more its accuracy drops, by 10-30 percentage points. This prompt reviews an AI response and flags every place warmth overrides accuracy.
An SEO-optimized 2,000-word blog post — target keyword research, H2/H3 structure, internal links, and meta description all generated in one pass.