AI Prompt Secret Codes
Shorthand prompt prefixes and trigger words that instantly shift AI behavior across all major platforms.
Last updated: July 2026
AI Prompt 'Secret Codes' are short prefixes and trigger words that work across ChatGPT, Claude, Gemini, Perplexity, and Grok. Add one to the start of your request and the AI shifts its behavior right away. You can steer tone, depth, format, and reasoning style without writing long instructions. MIT research found that half of AI performance gains come from how you prompt, not from the model itself.
“The right prefix turns an average response into one that fits exactly what you need. These codes are the shortest path between what you want and what the AI gives back, because they speak in the model's own language.”
Tone & Clarity Codes
| Code | Effect | Example Usage |
|---|---|---|
| ELI5 | Asks for a beginner-friendly, jargon-free explanation that a complete newcomer can follow | ELI5: How does quantum computing work? |
| JARGON | Switches to expert-level technical language for specialists in the field | JARGON: Explain transformer attention mechanisms |
| BRIEFLY | Asks for a very short, direct answer with no preamble and no filler | BRIEFLY: What is RAG? |
| DEEP DIVE | Expands into thorough detail, covering all angles and edge cases | DEEP DIVE: How does backpropagation work? |
| TONE: [style] | Switches writing style across the whole response | TONE: Conversational - Explain neural networks |
Role & Persona Codes
| Code | Effect | Example Usage |
|---|---|---|
| ACT AS [role] | Asks the AI to respond from a specific expert's point of view, with the right depth and vocabulary for that field | ACT AS a senior cybersecurity engineer - review this code for vulnerabilities |
| DEV MODE | Responds like a hands-on software engineer: code-first, practical, opinionated | DEV MODE: Build a FastAPI endpoint for JWT authentication |
| PM MODE | Adds project management structure: timelines, priorities, stakeholders, dependencies | PM MODE: Plan the rollout of this new AI feature |
| DEVIL'S ADVOCATE | Asks the AI to argue the opposing side of any topic or proposal | DEVIL'S ADVOCATE: We should migrate to microservices |
Analysis & Structure Codes
| Code | Effect | Example Usage |
|---|---|---|
| SWOT | Generates a Strengths, Weaknesses, Opportunities, Threats breakdown in a structured format | SWOT: Our company launching an AI product in 2025 |
| STEP-BY-STEP | Asks the AI to reason through each part in order. This is proven to reduce errors on complex tasks by activating chain-of-thought reasoning. | STEP-BY-STEP: Debug why this React component re-renders infinitely |
| RISK-ANALYSIS | Lists risks, how likely each one is, its possible impact, and ways to reduce it | RISK-ANALYSIS: Deploying LLMs in a healthcare setting |
| PRIORITIZE | Ranks a list by effort versus impact and points to the top 3 items with the most payoff | PRIORITIZE: These 8 features for our MVP launch |
| EXAMPLES | Asks for three concrete, real-world examples for any concept or claim | EXAMPLES: When RAG is better than fine-tuning |
Output Format Codes
| Code | Effect | Example Usage |
|---|---|---|
| SOURCES | Asks the AI to list credible references next to every factual claim in the answer | SOURCES: What are the best practices for AI evaluation? |
| PROMPT-TUNE | Asks the AI to improve your prompt for clarity first, then answer with the improved version | PROMPT-TUNE: Improve my original prompt and re-answer |
| TLDR | Asks for a summary at the top or bottom of a long response. Add 'TLDR at top' or 'TLDR at bottom'. | TLDR: Explain the difference between RAG and fine-tuning |
| TABLE | Structures any comparison, list, or analysis as a clean markdown table | TABLE: Compare LangChain vs LlamaIndex vs Haystack |
| CHECKLIST | Turns any process, plan, or recommendation into an actionable step-by-step checklist | CHECKLIST: Steps to deploy a RAG system to production |
ChatGPT (OpenAI) - Platform-Specific Techniques
ChatGPT responds well to clear reasoning instructions and to checking its own work. These techniques get more out of the platform.
Prompt
Chain of Thought: Add "Think step by step before answering" to any complex question. It prompts deeper internal reasoning. Self-Critique Mode: After any response, ask: "Now critique your own answer and improve it." This triggers an automatic revision cycle. Code Interpreter Trigger: Say "Use your Python tool to calculate..." to make ChatGPT write and run real code instead of estimating.
Output
More accurate, well-reasoned responses that show work, self-correct, and use real computation rather than approximation
Claude (Anthropic) - Platform-Specific Techniques
Claude works like a careful analyst. It follows exact formatting instructions better than competing models, and it shows its reasoning well when you ask it to.
Prompt
Show Your Work: "Show me how you think through each part." Claude is especially good at laying out its reasoning chain when you ask directly. Good/Bad Examples: Give one good example and one bad example. Claude picks up the difference faster than other models. Handoff Document: Before you reach Claude's context limit, ask: "Write a handoff document summarizing the plan, decisions made, and next steps." Paste this into the new session to keep full continuity.
Output
Transparent reasoning chains, precise format adherence, and accurate task continuation across context windows
Google Gemini - Platform-Specific Techniques
Gemini's official prompting guide puts specificity and role framing above everything else. It does well with long-context and multimodal tasks.
Prompt
Persona + Audience Combo: "You are a marketing educator for small business owners. Explain X for a beginner with $500/month budget." The trick is to combine both persona AND audience in every prompt. Long Context Hack: Gemini supports up to 1 million tokens. Paste whole documents and ask it to synthesize or cross-reference rather than summarizing by hand.
Output
Highly targeted responses optimized for the specific audience and reading level specified in the prompt
Perplexity AI - Platform-Specific Techniques
Perplexity's real strength is fast, sourced information retrieval. Use it differently from models built mainly for reasoning.
Prompt
Focus Mode: Use the Focus toggles (Academic, YouTube, Reddit, News, Wolfram Alpha) to narrow your search scope. Academic mode returns only peer-reviewed sources. Follow-Up Drilling: After any answer, try "Go deeper on point #3" or "Find contradicting evidence for this." Threading lets you refine your research without losing the citation context.
Output
Sourced, citation-backed answers with the ability to drill down into specific claims with traceable references
Grok (xAI) - Platform-Specific Techniques
Grok has live access to X/Twitter and is built to engage with edgy or controversial topics more openly than other models.
Prompt
Real-Time X Data: Ask Grok to "Search recent X posts about [topic]." It has live access to the X platform, which makes it handy for trending topics and public sentiment. Unfiltered Analysis: Grok takes on controversial topics more openly, which helps with red-teaming, satire, and taboo analysis prompts.
Output
Real-time sentiment analysis from X/Twitter data and candid takes on controversial topics that other models avoid
Advanced Universal Techniques
- 1Negation Prompting - Ask "What would we lose if X ceased to exist?" instead of "Why is X important?" You get deeper, less obvious insights, because the model has to reason from absence rather than presence.
- 2Multi-Lens Analysis - Add "Analyze this from an economic, psychological, and historical perspective simultaneously." This makes the model cross-reference frameworks and produce richer, less one-dimensional analysis.
- 3Constraint-Based Creativity - Add format constraints like "Explain in exactly 3 bullet points" or "Summarize as a haiku." Creative limits sharpen precision and surface unexpected angles, since the model has to prioritize hard.
- 4Domain-Specific Language (DSL) - For technical tasks, use the exact terms of the field. AI models trained on domain text answer more accurately when you match their training vocabulary. Don't say 'AI chat program', say 'LLM with a transformer architecture'.
- 5Iterative Prompt Refinement - After any response, use PROMPT-TUNE: Improve my original prompt and re-answer. The model rewrites your query into a stronger version and then runs it. Stack this twice for complex tasks.
- 6Insight & Depth Codes - "What does X truly optimize for?" reveals hidden incentives. "What is the hidden reality behind X?" gets past common narratives. "What paradox is central to X?" surfaces contradictions. These questions push past surface-level answers and expose truths that aren't obvious.
Weak vs. Strong: Getting Expert-Level Technical Depth
Weak prompt
Explain how transformer attention works.
Strong prompt
JARGON: ACT AS a machine learning researcher. STEP-BY-STEP: Explain scaled dot-product attention in transformer models, covering the mathematical formulation, why scaling by sqrt(d_k) prevents vanishing gradients, and when multi-head attention outperforms single-head. EXAMPLES: Include one concrete example with small matrices.
Output
A precise, technically rigorous explanation covering Q/K/V matrices, the scaling factor rationale, softmax stability, multi-head projection benefits - with a worked 3×3 matrix example showing attention weights computation.
Weak vs. Strong: Structured Competitive Analysis
Weak prompt
Compare LangChain and LlamaIndex.
Strong prompt
TABLE: Compare LangChain vs LlamaIndex vs Haystack across these dimensions: Primary use case, Learning curve, RAG support, Agent framework quality, Community size, Best for (type of developer). PRIORITIZE which to use for a team building a production RAG system with 5 engineers.
Output
A clean markdown comparison table across all 6 dimensions + a prioritized recommendation: LlamaIndex for RAG-first teams (reason: purpose-built for document retrieval with simpler abstractions), LangChain for teams needing complex agent chains, Haystack for enterprise-scale deployments requiring modularity.
Advanced: Using Claude's Context Preservation Across Sessions
Prompt
We've been designing a multi-agent customer support system for the past 3 sessions. Before I start a new chat, write a handoff document covering: (1) the final architecture we chose and why, (2) the 3 key decisions made and their rationale, (3) the open questions we haven't resolved, (4) the exact next step I should start with in the new session.
Output
A structured handoff document with architecture summary (orchestrator + 3 specialist agents), decision log (chose LangGraph over AutoGen for stateful workflows, Pinecone for vector store, Claude Sonnet for sub-agents), open questions (escalation logic, human handoff UX), and next step: 'Implement the ticket-routing classifier using the schema we defined in session 2.'
Frequently asked questions
What are AI prompt secret codes?
AI prompt secret codes are short prefixes or trigger words that modify the behavior of AI models like ChatGPT, Claude, and others. By adding these codes at the start of a request, users can adjust the tone, depth, format, and reasoning style of the AI's response without lengthy instructions.
How do AI prompt secret codes improve AI performance?
AI prompt secret codes enhance AI performance by optimizing how prompts are structured, accounting for up to half of the performance gains. These codes communicate in the model's own language, allowing users to get tailored responses that better fit their needs.
What are some examples of AI prompt secret codes?
Examples of AI prompt secret codes include 'ELI5' for beginner-friendly explanations, 'JARGON' for expert-level language, and 'DEEP DIVE' for detailed analysis. Each code serves a specific purpose, such as adjusting tone or providing structured analysis.
How can AI prompt secret codes change the tone of AI responses?
AI prompt secret codes like 'TONE: [style]' allow users to switch the writing style of AI responses. For instance, adding 'TONE: Conversational' can make explanations more informal and engaging, while 'JARGON' can shift to technical language for experts.
What platforms support AI prompt secret codes?
AI prompt secret codes are supported across major platforms including ChatGPT, Claude, Gemini, Perplexity, and Grok. These codes are versatile and can be used to modify AI behavior consistently across different AI models.