I am still on the fence about this.. I have a feeling that too much English filler is bad for the result. Could probably test this, honestly. I tend to use the LLM to write my prompts, then refactor it with reference material or guideline documents. I like to get it to widen the view it is seeing of the system (abstraction of sorts).. Then refactoring the prompt for efficiency and accuracy and then using that for generation. I will have it break the task down into logical groups and throw one piece at a time for a LLM to implement Structured prompts are easy to have a model generate from natural English. If you need the agent to stick to a plan like a code-planning.md where it will plot the tasks, remind itself of caveats, and update a section for "what we learned about the last edit in context to the system as a whole" - - structured with named functions is the only way IMO. Natural English it'll forget to check the file usually. Structured it seems to follow the process a bit better. I don't use that method often. Cline / claude-3.5-sonnet-beta Answer from halobreak on reddit.com
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OpenAI
platform.openai.com › docs › guides › prompt-engineering
Prompt engineering | OpenAI API
Use the Playground to develop and iterate on prompts. ... Ensure JSON data emitted from a model conforms to a JSON schema.
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Reddit
reddit.com › r/promptengineering › json prompting is exploding for precise ai responses, so i built a tool to make it easier
r/PromptEngineering on Reddit: JSON prompting is exploding for precise AI responses, so I built a tool to make it easier
August 29, 2025 -

JSON prompting is getting popular lately for generating more precise AI responses. I noticed there wasn't really a good tool to build these structured prompts quickly, so I decided to create one.

Meet JSON Prompter, a Chrome extension designed to make JSON prompt creation straightforward.

What it offers:

  • Interactive field builder for JSON prompts

  • Ready-made templates for video generation, content creation, and coding

  • Real-time JSON preview with validation

  • Support for nested objects

  • Zero data collection — everything stays local on your device

The source code is available on GitHub if you're curious about how it works or want to contribute!

Links:

  • Chrome Web Store: https://chromewebstore.google.com/detail/json-prompter/dbdaebdhkcfdcnaajfodagadnjnmahpm

  • GitHub: https://github.com/Afzal7/json-prompter

I'd appreciate any feedback on features, UI/UX or bugs you might encounter. Thanks! 🙏

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PromptLayer
blog.promptlayer.com › is-json-prompting-a-good-strategy
Is JSON Prompting a Good Strategy?
August 1, 2025 - Instead of feeding in natural language text blobs to LLMs and hoping they understand it, this strategy calls to send your query as a structured JSON. For example... rather than "Summarize the customer feedback about shipping", you
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Analytics Vidhya
analyticsvidhya.com › home › why i switched to json prompting and why you should too
Why I Switched to JSON Prompting and Why You Should Too
Instead of asking for a loose answer, you give the model a clear JSON format to follow: keys, values, nested fields, the whole thing. It keeps responses consistent, easy to parse, and perfect for workflows where you need clean, machine-readable output rather than paragraphs of text. Also Read: Learning Path to Become a Prompt Engineering Specialist
Published   November 6, 2025
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CodeConductor
codeconductor.ai › blog › structured-prompting-techniques-xml-json
Structured Prompting Techniques: XML & JSON Prompting Guide
October 9, 2025 - In this guide, we’ll explore what structured prompting really means, how XML and JSON prompting work, why they’re effective, and when to use each — complete with real-world examples and use cases.
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Reddit
reddit.com › r/promptengineering › do you write prompts in json logic or natural language?
r/PromptEngineering on Reddit: Do you write prompts in JSON logic or natural language?
November 27, 2024 -

Hi everyone,

I’ve been experimenting with prompt engineering and I’m curious, do you structure your prompts with a JSON-like logic (e.g., explicitly defining key-value pairs, conditions, etc.), or do you write them in plain natural language?

For those who’ve tried both, do you see a noticeable difference in accuracy or outcomes? Does one approach work better for certain types of tasks?

Looking forward to hearing your experiences and tips!

Top answer
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I am still on the fence about this.. I have a feeling that too much English filler is bad for the result. Could probably test this, honestly. I tend to use the LLM to write my prompts, then refactor it with reference material or guideline documents. I like to get it to widen the view it is seeing of the system (abstraction of sorts).. Then refactoring the prompt for efficiency and accuracy and then using that for generation. I will have it break the task down into logical groups and throw one piece at a time for a LLM to implement Structured prompts are easy to have a model generate from natural English. If you need the agent to stick to a plan like a code-planning.md where it will plot the tasks, remind itself of caveats, and update a section for "what we learned about the last edit in context to the system as a whole" - - structured with named functions is the only way IMO. Natural English it'll forget to check the file usually. Structured it seems to follow the process a bit better. I don't use that method often. Cline / claude-3.5-sonnet-beta
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So far just once, for a system prompt that takes a list of nouns and adjectives in Czech language and returns a list of inflected versions of said words in all grammatical cases. Back then, it was also a pretty good prompt for testing different models. The most important though, was proper and consistent output as a Javascript Object. Currently, almost all of my prompts are formatted in Markdown and for things like conditions, variables, etc. I just use some simple made up pseudocode. For larger prompts I also like to define some simple commands as /command or /command + parameters - these are always provided with command definition, syntax and some examples. Anyway, using JSON for writing prompts is certainly something, that I yet plan to explore. BTW, another interesting format, besides JSON, could be YAML.
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Apidog
apidog.com › blog › json-format-prompts
Boost AI Prompt Accuracy with JSON: A Technical Guide for Developers
August 5, 2025 - Over-Specification: Too many ... engineers, and technical teams, mastering JSON-formatted prompts is a practical way to control and standardize AI outputs....
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Teamcamp
teamcamp.app › resources › json-prompt-generator
Free JSON AI Prompt Generator for Smarter Workflows
Turn ideas into structured prompts in seconds. Convert natural language into JSON for AI task automation, API development, and for smarter workflows.
Find elsewhere
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AI Mind
pub.aimind.so › prompts-masterclass-output-formatting-json-5-3a5c177a9095
Prompts Masterclass: Output Formatting — JSON #5 | by Isaac Yimgaing | AI Mind
September 18, 2023 - Prompts Masterclass: The Complete Guide to Advanced Prompt Engineering for LLMs #1 · Prompts Masterclass: A Deep Dive into Variables with prompts #2 · Prompts Masterclass: A Deep Dive into Output Formats #3 · Prompts Masterclass: Output Formatting — Tables #4 · In our previous capsule, we delved into various ways of crafting prompts to generate different types of tables. But let’s be real! In most cases, the format most commonly used for communication between different applications is JSON...
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Medium
medium.com › coding-nexus › why-json-prompts-make-ai-more-reliable-with-code-real-examples-edf439999ce7
Why JSON Prompts Make AI More Reliable (With Code & Real Examples) | by Code Coup | Coding Nexus | Medium
July 31, 2025 - Why do AIs love this? They’re trained on tons of JSON from websites and apps. It’s like speaking their language. When you use JSON prompts, you’re telling the AI exactly what you want, and it’s way less likely to mess up.
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Superprompt
superprompt.pro › json-prompt-engineering.html
JSON Prompt Engineering for Product Teams — AI Superprompt Studio
Ship faster by reusing the same prompt system for multiple products, adjusting only product names, features and target audience. Everything happens in writing: you describe your tools and goals, I build and refine the JSON library in 1–2 feedback rounds.
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ChatGPT
chatgpt.com › g › g-dSb3dH8bt-json-prompt-creator
ChatGPT - JSON Prompt Creator
ChatGPT helps you get answers, find inspiration, and be more productive.
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Reddit
reddit.com › r/promptengineering › start directing ai like a pro with json prompts (guide and 10 json prompt templates to use)
r/PromptEngineering on Reddit: Start Directing AI like a Pro with JSON Prompts (Guide and 10 JSON Prompt Templates to use)
August 25, 2025 - Prompt engineering is the application of engineering practices to the development of prompts - i.e., inputs into generative models like GPT or Midjourney. ... TL;DR: Stop writing vague prompts.
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MachineLearningMastery
machinelearningmastery.com › home › blog › mastering json prompting for llms
Mastering JSON Prompting for LLMs - MachineLearningMastery.com
November 14, 2025 - Learn how to control LLM outputs using JSON prompting with schema design, Python implementation, and validation patterns.
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Microsoft Learn
learn.microsoft.com › en-us › ai-builder › change-prompt-output
JSON output - Microsoft Copilot Studio | Microsoft Learn
Text can be convenient for many uses cases; however, if the response has several elements that need to be identified individually, the text option can be limited. The JSON output lets you generate a JSON structure for your prompt response instead ...
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Substack
genaiunplugged.substack.com › p › structured-outputs-json-prompts-guide
How to Get Perfect JSON from AI Every Time: Structured Output Prompts That Never Break [Prompt Engineering Lesson 4]
November 25, 2025 - Schema: { “name”: “string”, “email”: “string”, “phone”: “string”, “company”: “string” } Perfect example output: { “name”: “Sarah Johnson”, “email”: “sarah.j@techcorp.com”, “phone”: “415-555-0123”, “company”: “TechCorp” } Strict rules: - Output ONLY the JSON object, no other text - Use empty string “” if any field is missing - Phone format must be XXX-XXX-XXXX - Match field names exactly as shown in schema Now process this text: [input text here] The model sees the perfect example first, understands the desired structure, then reads the rules that explain why it’s formatted that way. Even with perfect prompts, occasional errors happen.
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Medium
medium.com › @marketing_novita.ai › enhance-ai-models-prompt-engineering-with-json-output-ca450f62159a
Enhance AI Models Prompt Engineering with JSON Output | by Novita AI | Medium
May 26, 2025 - Highly dynamic or arbitrary schemas, such as lists of key-value pairs where keys are not predefined, are difficult to implement with structured outputs. In such cases, standard JSON mode with instructions in the system prompt may be more effective.
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LobeHub
lobehub.com › home › agents › json prompt generator
JSON Prompt Generator | AI Agents / GPTs · LobeHub
November 11, 2025 - This tool is ideal for professionals needing clear, structured instructions for automated task execution, ensuring clarity and consistency in prompt engineering. Its core value lies in transforming ambiguous or complex task requests into ...