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developers.openai.com › api › docs › guides › prompt-engineering

Prompt engineering | OpenAI API

Enhance results with prompt engineering strategies. With the OpenAI API, you can use a large language model to generate text from a prompt, as you might using ChatGPT. Models can generate almost any kind of text response—like code, mathematical equations, structured JSON data, or human-like prose. Here’s a simple example using the Responses API.

MachineLearningMastery

machinelearningmastery.com › home › blog › mastering json prompting for llms

Mastering JSON Prompting for LLMs - MachineLearningMastery.com

November 14, 2025 - At inference time, JSON prompting effectively constrains the token space — the model learns to predict tokens that match the requested structure. For instance, consider this instruction: A well-trained LLM like GPT-4 or Claude 3 will now return: This output can be immediately parsed, stored, or processed by Python applications without additional cleaning.

Videos

Create Insane AI Videos By 10X'ing Your JSON Prompts - YouTube

August 12, 2025

Why JSON Prompting is REPLACING Traditional Methods - YouTube

What Are JSON Prompts? Veo 3 JSON Prompting Explained. - YouTube

How To Use Google VEO 3 JSON Prompting To Create $100k AI Ads - ...

This Is How To Use Google Veo 3 Like A PRO: JSON Prompt (The Only ...

How To Create JSON Prompts for AI Videos: VEO 3 - YouTube

vladris.com › llm-book › prompt-engineering.html

Prompt Engineering ~ LLMs at Work

We define a static from_file() ... template JSON from the given template_file and constructs a Template object based on that. Finally, we define a completion() method that takes some parameters (in our case, these would be the actual action and document), makes a deep copy of the prompt template which it updates with the given parameters, then calls the OpenAI Completion API, and expand the parameters available in template. ... In Python, **kwargs ...

build5nines.com › artificial intelligence › prompt engineering › how to write ai prompts that output valid json data

How To Write AI Prompts That Output Valid JSON Data | Build5Nines

April 8, 2025 - Even well-prompted models sometimes return extra text, incorrect brackets, or malformed syntax. Build safeguards into your workflow: Validate the output using a parser like json.loads() in Python

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 - Indicate that a JSON schema will be provided (see Step 5 below) to maintain a consistent and valid structure. Use one-shot prompting with a sample output to guide the model; add more examples if needed for consistency, but avoid too many, as they may limit the model’s ability to handle new inputs. Define how to handle missing or invalid data. For demonstration purposes, we’ll include a review text in the prompt as a Python variable:

nobledesktop.com › prompt engineering with json and jinja in python labs

Prompt Engineering with JSON and Jinja in Python Labs - Free Video Tutorial

October 5, 2024 - This is prompt engineering. “Please provide an example of a great Hollywood movie from the 1990s. Describe the movie and include important facts such as the title, year, stars, and director.” · Next we’ll try to get the data back as JSON. I’ll get the AI answer as JSON and publish that.

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.

OpenAI Developer Community

community.openai.com › api

Prompt integrating JSON, or JSON request after the prompt - API - OpenAI Developer Community

April 27, 2023 - Hello everyone, I am completely new to coding and development, but I had a question: in the context of a quiz, to ensure the results obtained, what do you recommend: Option 1: Write a very structured prompt and use a very “simple” python script without json or anything else to ensure the correct processing of tasks (I have managed to get the script to correctly analyze the response for 9 out of 10 API calls, but there is still this 1/10 where the format of the response poses a problem) Option...

realpython.com › practical-prompt-engineering

Prompt Engineering: A Practical Example – Real Python

May 3, 2024 - You’ve decided to switch to an even more powerful LLM, GPT-4. In the rest of this tutorial, you’ll use GPT-4 to continue exploring other important prompt engineering techniques: Role prompting: Using a system message to set the tone of the conversation, and using different roles to give context through labeling · Chain-of-thought prompting (CoT): Giving the model time to think by prompting it to reason about a task, then including the reasoning in the prompt · You’ll also use GPT-4 to classify the sentiment of each chat conversation and structure the output format as JSON.

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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 12, 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

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 ...

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.

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 - A single prompt could trigger multiple actions, each with specific parameters and expected outputs. Quality Control JSON structure makes it easy to validate outputs. You can check if all required fields are present, if data types match expectations, and if values fall within acceptable ranges. Consider this real-world example from a client I worked with in 2023:

Analytics Vidhya

analyticsvidhya.com › home › why i switched to json prompting and why you should too

What is JSON Prompting? [Examples, Tips and More]

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

Prompt Engineering

engineering.prompt.cards › en › posts › json-driven-prompting

Use JSON for prompting - Prompt Engineering

April 16, 2024 - For example: { "outline": "10 big plot points", "chapters": 12, "words_per_chapter": 2500, "query_letter": "300-word pitch" } A simple json.loads() in Python (or the equivalent in JavaScript, Ruby, etc.) gives you native objects you can loop over, validate, transform, and send straight into ...

marktechpost.com › home › technology › ai shorts › json prompting for llms: a practical guide with python coding examples

JSON Prompting for LLMs: A Practical Guide with Python Coding Examples - MarkTechPost

August 25, 2025 - When you frame your prompt in JSON, ... In this example, asking for a market summary, sentiment, opportunities, risks, and a confidence score can yield inconsistent formats when passed as plain text....

linkedin.com › pulse › prompt-engineering-json-yaml-why-has-edge-robyn-le-sueur-gmhif

Prompt Engineering: JSON or YAML? Why YAML has the ...

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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 ... ... TL;DR: Stop writing vague prompts. Use a structured JSON format to tell the AI exactly what you want, how you want it, and in what format....

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 - Examples include: ... JSON Schema provides a standardized way to define the expected structure and data types of the output. Validation ensures that outputs conform to predefined schemas, enabling automatic error detection and preventing malformed data. JSON supports optional fields, allowing flexibility in schema design. Tools like Pydantic in Python enable developers to define schemas with optional type annotations, handling cases where certain fields may not always be present.