Reasoning and verification
Prompts that demand visible assumptions and checkable intermediate steps.
24 curated prompts
DeepSeek Prompts
DeepSeek models are widely used for reasoning-heavy and code-heavy workloads. These prompts focus on verifiable reasoning, explicit assumption tracking, and outputs that survive review by a second model or a human reviewer.
DeepSeek promptsDeepSeek R1 promptsDeepSeek prompt templates
Competitive coding
Algorithm design, complexity analysis and edge-case enumeration.
Batch analysis at low cost
Templates for high-volume classification and extraction with fixed schemas.
Featured AI prompt templates
Copy-ready prompts selected from this topic cluster.
TextCoding
Comprehensive Codebase Auditor
Exhaustively analyze entire codebase to identify, classify, and prioritize all verifiable bugs, security vulnerabilities, and critical issues.
TextLogic Reasoning
Constraint Typology Architect
Design constraint-based workflows for LLM planning systems that reliably capture user intent through two high-level types: hard rules (never violated, exhaustively verified) and soft preferences (flexible, contextually judged), avoiding mixed verification styles or numeric weights that erode trust and plan reliability.
CodeCoding
Paper-to-Code Research Implementer
Transforms academic papers (especially arXiv ML/AI papers) into minimal, honest, verifiable Python implementations, strictly anchored to paper content without inventing unspecified details.
TextLogic Reasoning
AI Co-Mathematician
An interactive, stateful research partner for mathematicians working on open-ended problems, supporting the entire lifecycle of mathematical discovery: ideation, literature search, computational exploration, conjecture formation, theorem proving, and theory building. This prompt emphasizes exploratory, iterative collaboration over simple problem-solving.
TextCoding
Coding Agent System Prompt
A system prompt for a software engineering assistant embedded in the command-line development environment, guiding AI to complete programming tasks such as coding, debugging, refactoring, and test management. Emphasizes safe operations, cautious execution of changes, preference for dedicated tools, and requires output in GitHub-flavored Markdown format.
TextLogic Reasoning
Reasoning Specialist: Guided Complex Problem Decomposition and Structured Thinking
This prompt guides the model to decompose complex problems systematically using reasoning frameworks like Chain-of-Thought (CoT) and Tree-of-Thoughts (ToT), emphasizing hypothesis validation, constraint analysis, and confidence assessment for deep logical reasoning tasks.
TextWriting
Prompt Engineering Expert
Prompt from prompts.chat: Prompt Engineering Expert
TextLogic Reasoning
Autonomous Permission Classifier Architect
Design a safety layer for a coding agent that runs autonomously, deciding which actions proceed unsupervised, which are blocked, and how the agent recovers—balancing 'ask every time' vs 'skip all permissions'.
TextCoding
PCB/EDA Design Architect
This prompt defines a senior PCB/EDA design architect responsible for end-to-end review of electronic designs, covering schematic, PCB layout, signal integrity, power integrity, EMC pre-compliance, SPICE simulation, and DFM analysis, delivering structured engineering reports.
TextLogic Reasoning
LLM Judge Routing Strategist
Design cost-efficient, distribution-shift-robust routing policies to dynamically assign queries between reasoning and non-reasoning LLM judges under a fixed compute budget, optimizing accuracy-cost trade-offs.
TextLogic Reasoning
Reasoning Model Prompting Guide
Best practices and templates for prompting reasoning models (o1, Claude 3.7, Gemini 2.0 Thinking) that run internal chain-of-thought before responding.
How should prompts change for reasoning models?
Do not ask for a chain of thought. Instead require a short justification, stated assumptions, and a final answer that can be checked independently.
Are these prompts useful for self-hosted deployments?
Yes. They avoid proprietary features and rely on instruction structure rather than vendor-specific tooling.
How do I compare DeepSeek against other models fairly?
Reuse the same prompt and rubric across models, then score outputs on the same criteria rather than comparing impressions.