Turn Any AI Into a Precision Tool - The most underrated AI skill of 2025
After months of experimenting with AI tools across my agency, I've discovered something that completely changed how I work with language models: JSON prompting. This technique transforms ChatGPT, Claude, and Gemini from rambling conversation partners into precise, structured agents that deliver exactly what you need, every time.
JSON prompting is simply putting your instructions inside a structured format instead of asking casual questions.
Instead of this:
"Can you write a tweet about dopamine detox?"
You do this:
{
"task": "write a tweet",
"topic": "dopamine detox",
"style": "viral",
"length": "under 280 characters"
}
Not English. Not vibes. Just instructions, like filling out a form.
Here's the key insight: LLMs don't "understand" language the way humans do. They follow patterns and structure. JSON is ultra-structured, leaving zero ambiguity. You're not asking—you're specifying exactly what you want.
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Think of it like this:
GPT, Claude, and Gemini were all trained on massive amounts of code, APIs, and structured data. JSON looks like the high-quality, structured content they learned from. When you use JSON, you're speaking their native language.
The less they have to guess about your intent, the better the results.
1. Use key-value pairs
{
"task": "your main request",
"goal": "what you want to achieve"
}
2. Be explicit about everything
{
"task": "generate a list",
"topic": "books that improve thinking",
"audience": "young entrepreneurs",
"output_format": "markdown bullets"
}
3. Use nested objects for complex structure
{
"task": "write a thread",
"platform": "twitter",
"structure": {
"hook": "strong, short, curiosity-driven",
"body": "3 core insights with examples",
"cta": "ask a question to spark replies"
},
"topic": "founder productivity systems"
}
Normal prompt: "Recommend books that help me think clearer"
JSON prompt:
{
"task": "recommend books",
"topic": "thinking clearly",
"audience": "entrepreneurs",
"criteria": "practical, actionable insights",
"output_format": "list of 5 with one-sentence summaries",
"tone": "confident and direct"
}
Try both approaches. The JSON version will be crisper, more relevant, and immediately usable.
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{
"task": "write content",
"platform": "twitter",
"structure": {
"hook": "short, curiosity-driven",
"body": "3 insights with smooth flow",
"action": "1 strong question to spark replies"
},
"topic": "[YOUR TOPIC]",
"tone": "relatable and smart",
"length": "under 280 characters"
}
Advanced version for long-form content:
{
"task": "write blog post",
"topic": "remote team productivity",
"target_audience": {
"role": "startup founders",
"experience": "first-time managers",
"pain_points": ["communication gaps", "timezone coordination"]
},
"structure": {
"intro": "hook with surprising stat",
"main_points": 4,
"examples": "real company case studies",
"conclusion": "actionable next steps"
},
"tone": "authoritative but approachable",
"length": "1500-2000 words"
}
{
"task": "generate video script",
"platform": "Veo",
"video_type": "explainer",
"topic": "how to start a dropshipping store",
"duration": "60 seconds",
"target_audience": "complete beginners",
"voiceover": {
"style": "calm and confident",
"accent": "US English",
"pace": "medium"
},
"visual_style": "modern, clean, fast cuts",
"call_to_action": "visit website for free guide"
}
{
"task": "write code",
"language": "python",
"goal": "build a script that renames all files in a folder",
"requirements": [
"must work on MacOS",
"include error handling",
"add progress indicator"
],
"constraints": [
"no external dependencies",
"include detailed comments"
],
"output_format": "code only, no explanation"
}
For debugging:
{
"task": "debug code",
"language": "javascript",
"problem": "function returns undefined instead of calculated value",
"code": "[PASTE YOUR CODE HERE]",
"goal": "identify bug and provide corrected version",
"explanation_level": "beginner-friendly"
}
{
"task": "act as business consultant",
"client_context": {
"industry": "SaaS",
"stage": "early-stage startup",
"team_size": "5 people",
"current_challenge": "product-market fit"
},
"goal": "define clear go-to-market strategy",
"deliverables": [
"target customer persona",
"value proposition statement",
"3 key differentiators",
"marketing channel priorities"
],
"constraints": [
"budget under $10k/month",
"timeline: 3 months"
],
"tone": "strategic but practical"
}
{
"task": "analyze market trends",
"industry": "AI automation tools",
"focus_areas": [
"pricing models",
"feature differentiation",
"customer acquisition strategies"
],
"data_sources": "publicly available information",
"output_format": {
"executive_summary": "2 paragraphs",
"key_findings": "5 bullet points",
"recommendations": "3 actionable items"
},
"audience": "startup founders"
}
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{
"task": "write cold email",
"audience": "SaaS founders",
"product": "AI sales automation tool",
"goal": "book a 15-minute call",
"personalization": {
"company_name": "[COMPANY]",
"recent_achievement": "[SPECIFIC_WIN]"
},
"tone": "friendly but confident",
"length": "under 150 words",
"structure": {
"opening": "personalized compliment",
"value_prop": "specific benefit",
"social_proof": "brief case study",
"cta": "low-pressure meeting request"
}
}
{
"task": "improve existing content",
"input": "[PASTE ORIGINAL CONTENT HERE]",
"improvements_needed": [
"make more engaging",
"add specific examples",
"improve flow"
],
"target_audience": "busy executives",
"tone": "professional but conversational",
"constraints": [
"maintain original key points",
"keep similar length"
]
}
{
"task": "optimize content for SEO",
"primary_keyword": "AI productivity tools",
"secondary_keywords": [
"automation software",
"business efficiency",
"workflow optimization"
],
"content_type": "blog post",
"target_audience": "small business owners",
"optimization_focus": [
"keyword density 1-2%",
"semantic keywords",
"readable headers",
"meta description"
],
"length": "2000 words"
}
{
"task": "create social media content calendar",
"platform": "LinkedIn",
"duration": "30 days",
"brand": "B2B SaaS company",
"content_pillars": [
"industry insights",
"behind-the-scenes",
"customer success stories",
"thought leadership"
],
"posting_frequency": "5 times per week",
"goal": "increase engagement by 50%",
"output_format": "table with dates, topics, and post types"
}
{
"task": "create training document",
"topic": "customer onboarding process",
"audience": "new team members",
"format": "step-by-step guide",
"sections": [
"overview and goals",
"required tools and access",
"detailed process steps",
"common issues and solutions",
"success metrics"
],
"tone": "clear and supportive",
"include": [
"screenshots placeholders",
"checklists for each step"
]
}
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For complex tasks, break them into stages:
{
"task": "multi-stage content creation",
"stage_1": {
"task": "research and outline",
"topic": "AI in healthcare",
"deliverable": "structured outline with key points"
},
"stage_2": {
"task": "write first draft",
"input": "use outline from stage 1",
"length": "2000 words"
},
"stage_3": {
"task": "edit and polish",
"focus": ["clarity", "flow", "engagement"]
}
}
{
"task": "write product description",
"product_type": "software",
"if_b2b": {
"focus": "ROI and efficiency gains",
"tone": "professional and data-driven"
},
"if_b2c": {
"focus": "ease of use and benefits",
"tone": "friendly and accessible"
},
"target": "b2b"
}
{
"task": "generate email sequence",
"sequence_type": "welcome series",
"variables": {
"company_name": "[COMPANY]",
"product_name": "[PRODUCT]",
"user_name": "[USER]"
},
"emails": [
{
"day": 0,
"subject": "Welcome to [COMPANY], [USER]!",
"goal": "warm welcome and set expectations"
},
{
"day": 3,
"subject": "Getting started with [PRODUCT]",
"goal": "drive first key action"
},
{
"day": 7,
"subject": "Your first week with [PRODUCT]",
"goal": "check progress and offer help"
}
]
}
JSON isn't always the answer. Avoid it when you want pure creativity, conversational flow, exploratory thinking, or emotional support. Rule of thumb: JSON = structure and precision. Freeform = creativity and exploration.
Having trouble deciding when to use JSON vs freeform? Our community has decision trees and frameworks for this - join the discussion in Discord or Telegram
Track these metrics to improve your JSON prompting:
Stop "asking" AI for things. Start specifying exactly what you want, how you want it, and for whom. JSON prompting transforms AI from a creative writing partner into a precision business tool.
The professionals who master structured prompting today will have a massive advantage as AI becomes more central to business operations. While others are still having conversations with their AI tools, you'll be running precision workflows.
Start small. Pick one of the templates from this guide and adapt it to your most common AI task. Once you see the difference in output quality and consistency, you'll never go back to unstructured prompting.
The age of AI precision has begun. JSON prompting is your blueprint for success.
Want to take your AI automation to the next level? Check out our comprehensive guide: The Phone Botting Setup Guide
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