Code generation, debugging and documentation
39 prompts
Our product: [description]. Team size: [n]. Expected scale: [scale]. Should we use a monolith, modular monolith or microservices? Compare them for our situation, recommend one, and outline how to keep options open for the future.
Helps decide the right architecture for your team and product.
Outline and code a simple RAG prototype in Python that answers questions from [documents]. Cover loading and chunking documents, creating embeddings, storing them in [vector store], retrieving relevant chunks, building the prompt, and citing sources in answers.
Answers questions from your own documents using embeddings.
Explain this pull request diff for a reviewer. Summarise what changed and why (as far as you can tell), list risky areas, possible bugs, missing tests, and questions to ask the author. Diff: [paste diff]
Summarises what a PR changes and what to watch for.
Review this PostgreSQL schema. Check normalisation, data types, constraints, foreign keys, indexes for likely queries ([queries]), naming consistency, and future scaling issues. Suggest improvements with migration SQL. Schema: [paste schema]
Reviews a database schema for performance and integrity issues.
I want to build [app idea] for [users]. Suggest a simple tech stack for a solo developer, the core MVP features, a data model, main pages/screens, API endpoints, auth approach, hosting, and a 4-week build plan. Flag what to leave out of the MVP.
Turns an app idea into an MVP plan with stack, data model and milestones.
You are a bot developer. Platform: [Discord / Telegram] Language: [Python / JavaScript] Features: [commands, welcome messages, reminders, polls, moderation] Write the bot with a clear structure, command handlers, permission checks, error handling, and environment variables for the token (names only). Include setup ste…
Build a simple community bot with commands, moderation helpers or notifications.
You are a machine learning engineer. Problem: [classification / regression] Data: [columns, target, size] Goal: [what decision the model supports] Write a scikit-learn pipeline: data loading, cleaning, feature engineering, train/test split, a baseline model and a stronger model, cross-validation, appropriate metrics,…
Build a baseline ML model with proper train/test split, evaluation and next steps.
You are an engineer who builds production AI features. Feature idea: [e.g. summarise support tickets, auto-tag products] Stack: [details] Data involved: [types, sensitivity] Design: the prompt and system instructions, input/output format (JSON schema), handling of errors and timeouts, cost and latency considerations,…
Plan and build an AI feature, such as summarising, classifying or a chat assistant, with good prompts and guardrails.
Be my patient pair-programming partner. Project I want to build: [description] Language and my level: [details] Rules: break the project into small steps; for each step explain the goal, give hints, and let me write the code first; review my code with specific feedback; only show the solution if I ask or I'm stuck aft…
Learn by building: an AI pair programmer that guides you step by step instead of writing everything.
You are a cloud solutions architect. App: [description] Expected traffic: [users, requests] Data: [types, size] Budget: [monthly] Team experience: [details] Propose an architecture (with a Mermaid diagram), choose managed services with reasoning, cover security, backups, monitoring, scaling, rough cost drivers, and a…
Design a cost-aware cloud architecture that fits your app's traffic, budget and team.
You are a backend engineer. Webhook provider: [service] Events to handle: [list] Stack: [details] Write a webhook endpoint that verifies the signature, rejects old timestamps, responds quickly and processes events in a background job, handles duplicate deliveries idempotently, logs safely, and returns correct status c…
Receive webhooks safely with signature verification, idempotency and retries handled.
You are a performance-focused backend engineer. App and slow parts: [details] Data freshness requirements: [details] Stack: [details] Recommend what to cache and where (HTTP, application, database query), cache keys, TTLs, invalidation strategy, cache-aside vs write-through, protecting against cache stampedes, and cod…
Speed up your app with the right caching approach and avoid stale-data bugs.