How to Prompt AI for Consistent JSON Responses

Source: Dev.to

This was originally posted on the VSL Substack publication.

Bad JSON Means High Costs

When you are building features that depend on structured data like API integrations, database operations, or configuration files, JSON consistency directly determines whether your app works or breaks. A single malformed response can quickly cascade into hundreds of failed user actions, corrupt database writes, and support tickets flooding your inbox.

While AI generates JSON that looks correct during development, production environments expose inconsistencies such as:

• missing commas or trailing commas in strict parsers
• unescaped quotes in user‑generated content
• Unicode characters from international users

These small formatting errors crash entire features, so you need AI to output valid and production‑ready JSON consistently instead of just succeeding in your local tests.

The Airport Security Model

Airport security examines your documents multiple times at check‑in, security, and the gate because mistakes are expensive to fix later. JSON validation works the same way: AI may generate structurally sound JSON in controlled conditions but often breaks under real‑world variance. Building multiple validation checkpoints catches these errors before they reach production.

Most builders stop at the prompt, but production‑ready apps require five distinct checkpoints to ensure reliability.

The Most Common Ways AI Breaks Your Data

• Syntax Errors – Missing or extra commas, brackets, or quotes break JSON.parse() immediately. Example: a trailing comma after the last property or an unclosed nested object.
• Escaped Character Traps – Double‑escaping characters inside the JSON string (e.g., \\" instead of \") creates a technically valid string but invalid JSON for your parser.
• Structure Mismatch – Receiving a correctly formatted JSON object that doesn’t match the expected shape, e.g. {"title":"My Article"} when your code expects {"article":{"title":"My Article"}}.
• Type Confusion – Returning strings when numbers are required, or arrays when objects are expected. Example: "25" (string) instead of 25 (number).
• Truncated Responses – Large payloads can be cut off by the model’s token limit, leaving an incomplete JSON fragment. The reliable fix is to request data in smaller chunks and concatenate the results in your code.

The 5‑Checkpoint Framework for Bulletproof JSON

1. Be Explicit in Your Prompt

Don’t say: “Return the SEO data as JSON.”

Say: “Return ONLY valid JSON with no additional text. Use this exact structure: {\"title\": string, \"description\": string}.”

2. Provide a JSON Schema

The most reliable way to get consistent JSON structure is to give AI an exact schema to follow. JSON Schema defines the structure, types, and requirements for your data, eliminating ambiguity.

Add this to your prompt:

{
  "type": "object",
  "properties": {
    "title": {
      "type": "string",
      "maxLength": 70,
      "description": "An engaging, SEO‑friendly article title"
    },
    "description": {
      "type": "string",
      "maxLength": 160,
      "description": "A concise summary of the article content"
    }
  },
  "required": ["title", "description"],
  "additionalProperties": false
}

Why this works: Schemas eliminate type confusion and structure mismatch because the model validates its output against the schema pattern during generation. The additionalProperties: false flag forbids hallucinated extra fields, catching errors before they reach your code. Learn more at the OpenAPI Schema specification.

3. Request Code Fences

Add to your prompt: “Wrap the JSON in markdown code fences with json syntax highlighting.”

This prevents AI from adding explanatory text before or after the JSON, which would break parsing when you extract the response.

4. Validate Before Using

Never assume AI output is valid. Always wrap parsing in a try‑catch block:

try {
  const data = JSON.parse(aiResponse);
  // Use data here
} catch (error) {
  console.error('Invalid JSON from AI:', error);
  // Handle the error gracefully
}

5. Check Structure After Parsing

Valid JSON doesn’t guarantee the correct shape. Perform a lightweight validation after parsing:

function validateSeoData(data) {
  if (!data.title) return false;
  if (typeof data.title !== 'string') return false;
  if (typeof data.description !== 'string') return false;
  return true;
}

Red Flags: What to Watch For

• 🚩 AI adds explanation text before or after JSON – this breaks parsing.
• 🚩 Missing or extra commas, brackets, or quotes – leads to immediate JSON.parse failures.
• 🚩 Double‑escaped characters – creates strings that are not valid JSON.
• 🚩 Unexpected structure or types – causes downstream logic errors.
• 🚩 Truncated output – results in incomplete, unparsable payloads.

By incorporating the five checkpoints above and staying vigilant for these red flags, you can reliably obtain clean, production‑ready JSON from AI models every time.