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Do you write prompts in JSON logic or natural language?

reddit.com › r › PromptEngineering › comments › 1h0yvj1 › do_you_write_prompts_in_json_logic_or_natural

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

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.

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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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!

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

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.

ai.google.dev › gemini api › prompt design strategies

Prompt design strategies | Gemini API | Google AI for Developers

The output prefix gives the model information about what's expected as a response. For example, the output prefix "JSON:" signals to the model that the output should be in JSON format.

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! 🙏

The concept is “structure” works and JSON is structured. JSON is very wasteful from a token perspective, so other structured formats might make more sense, like markdown or xml

While I'd personally just use a quick Python script because I'm working on the API side, I can see how this would be nice for a chat interface user. I recently made a Chrome extension for a different task.... so fuckin useful. Jury is still out on JSON prompting. While it's occasionally unavoidable for multiturn prompts where you're dealing with structured output in other turns, there was one frontier lab engineer on X if I recall, who poo-poos the idea because Markdown works just as well. But, psychologically, you're also forcing the user to write a structured document so it's likely of higher quality. Personally, I've begun leaning towards XML for structured prompts. While the tags can eat up token count, I do like sneaking in additional metadata and context through tag attributes. But I suppose you can do that with JSON too as the LLM can get additional keywords and a JSON parser should strip them later if they're undefined in a formal schema.

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.

reddit.com › r/localllama › prompt engineering: how to get open source llms to just return a single value or json output?

r/LocalLLaMA on Reddit: Prompt Engineering: How to get open source LLMs to just return a single value or JSON output?

December 14, 2022 -

I’m trying the airoboros 33B model and other similar models but given a task they always give lengthy explanations which makes it harder to use as an API that can extract the result. Even after saying only return a value etc. they either stop working or don’t listen. Anyone has any insight?

You want to use the guidance library from MS. It's great for this.

I was JUST doing this for a work project. I really like the suggestion of the MS guidance library What I did, is define 2-3 manual examples of what I’m wanting. Then I had GPT4 create more 20 examples following my original JSON output example. Then embedded that in my prompt, before giving the task at hand

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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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 ... individually, the text option can be limited. The JSON output lets you generate a JSON structure for your prompt response instead of text....

leonnicholls.medium.com › the-json-whisperer-how-to-craft-prompts-that-make-google-gemini-sing-d9f91f714c6f

The JSON Whisperer: How to Craft Prompts That Make Google Gemini Sing | by Leon Nicholls | Medium

May 28, 2024 - As you can see, JSON is like a Swiss Army knife for prompt engineering. It’s simple, versatile, and powerful.

apidog.com › blog › json-format-prompts

Boost AI Prompt Accuracy with JSON: A Technical Guide for Developers

August 5, 2025 - Discover how using JSON-formatted prompts with AI models leads to more accurate, reliable, and testable outputs. Learn step-by-step techniques, best practices, and how Apidog helps developers streamline prompt engineering and API integration.

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...

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.

mpgone.com › home › blog › json prompt: the ultimate guide in 2026 to perfect ai outputs

JSON Prompt: The Ultimate Guide in 2026 to Perfect AI Outputs - MPG ONE

January 9, 2026 - JSON prompting is a game changing approach to AI communication that uses structured data formats instead of plain text and we tested for you in 2026, by organizing instructions as key value pairs, arrays, and objects, JSON prompts eliminate ...

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.

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.

aws.amazon.com › blogs › machine-learning › structured-data-response-with-amazon-bedrock-prompt-engineering-and-tool-use

Structured data response with Amazon Bedrock: Prompt Engineering and Tool Use | Artificial Intelligence

June 26, 2025 - Prompt engineering involves crafting precise input prompts to guide large language models (LLMs) in producing consistent and structured responses. It is a fundamental technique for developing Generative AI applications, particularly when structured ...

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 - Here’s my take on JSON prompts, explained simply, with some code.

blog.devgenius.io › how-to-get-gpt3-to-output-in-json-4e14c46aa5b6

How to get GPT3 to Output in JSON | by Erwin Russel | Dev Genius

February 16, 2023 - The app we made interfaced with the OpenAI API to send our scanned letter with our engineered prompt to in turn receive the output generated by the text-DaVinci-03 model. Our prompt was engineered in such a way that the text the model outputted was in JSON format and could be easily parsed.