The prompt library
we actually use
No prompt-marketplace bloat. Each prompt lists the use case it solves and the audience it is tuned for, across engineering, product, security, governance, and leadership.
Architecture review: second opinion
Sanity-check an in-progress design doc against unstated risks before commit.
View prompt →Threat model an LLM feature
Run a 10-minute threat model for any feature that calls an LLM.
View prompt →Founder: weekly priority cut
Convert a messy backlog into the one thing that moves the needle this week.
View prompt →Prompt regression eval skeleton
Generate an eval harness for a prompt you're about to change in production.
View prompt →EU AI Act: risk-tier classifier
Classify an AI system under the EU AI Act's risk tiers and list the obligations that follow.
View prompt →Internal AI usage policy: one-pager
Draft a plain-language acceptable-use policy for AI tools your staff can actually follow.
View prompt →Role-based AI literacy quiz
Generate a short, role-specific quiz to gauge a team's real AI fluency before training.
View prompt →Explain an AI concept to a skeptic
Turn a technical AI concept into a crisp explanation a doubtful stakeholder will accept.
View prompt →AI use-case prioritizer
Score and sequence a list of candidate AI use cases by value, feasibility, and risk.
View prompt →Build vs. buy vs. wait memo
Get a one-page decision memo for an AI capability you're deciding how to source.
View prompt →AI initiative: back-of-envelope ROI
Sanity-check the payback on an AI initiative before you commit budget.
View prompt →Data readiness gap scan
Find the data gaps that would block an AI use case before you start building it.
View prompt →AI vendor / model evaluation rubric
Build a scoring rubric to compare AI vendors or models on more than the demo.
View prompt →RAG retrieval debugger
Diagnose why a RAG system is returning weak or wrong context.
View prompt →Agent tool / function spec writer
Write a clean, model-friendly tool definition for an LLM agent to call.
View prompt →Data pipeline design review
Pressure-test an ETL / data-pipeline design before you build it.
View prompt →Cloud cost guardrails for AI workloads
Put guardrails on AI/inference spend before the bill surprises you.
View prompt →UX patterns for AI uncertainty
Design how your interface should behave when the AI is unsure, slow, or wrong.
View prompt →Prompt-injection test suite generator
Generate adversarial test cases to probe an LLM feature for prompt injection.
View prompt →LLM feature incident runbook
Draft an on-call runbook for when an AI feature misbehaves in production.
View prompt →Model drift & quality monitoring plan
Set up monitoring that catches quality regressions and drift after launch.
View prompt →AI feature handover checklist
Make sure a team can fully own an AI feature after the builders step away: the keys-in-hand checklist.
View prompt →On-call triage: my AI feature is acting up
A fast triage walkthrough when an LLM feature starts misbehaving and you're on call.
View prompt →These are the prompts behind production work. When you want the workflows and apps they sit inside built and handed over, that is Build & Run.
See Build & Run →