How to Clean Up Verbose AI Output in API Responses

Strategy 2: Constraining the Output Format (Speaking the Right Language)

While prompt engineering is powerful, you can achieve even greater reliability by instructing the model to respond in a structured format. This is where you move from hints to hard rules.

Demand JSON (and Use JSON Mode)

JSON is the universal language of APIs. Asking the AI to respond with JSON is the single most effective way to get clean, machine-readable data. Modern APIs, like OpenAI's, offer a dedicated "JSON Mode" that forces the output to be a syntactically correct JSON object.

When using JSON mode, you must include the word "JSON" in your prompt. Combine this with role-playing for best results:

You are a recipe information API. Based on the user's request, provide the recipe name, a list of ingredients, and the approximate cooking time in minutes. Your response must be a single, valid JSON object.

Define a Schema

Don't just ask for JSON; provide the exact schema you want it to follow. This dramatically reduces the chance of the AI inventing fields or getting creative with data types.

...Your response must be a JSON object that adheres to the following schema:

{
  "recipeName": "string",
  "ingredients": ["string"],
  "cookTimeMinutes": "integer"
}

Advanced: Function Calling / Tool Use

For maximum control, use the function calling (or "tool use") feature available in advanced models. Instead of having the AI generate a direct answer, you define a set of "tools" (functions in your code) that it can choose to call. The API response isn't the answer itself, but a structured JSON object telling you which tool to run and with what arguments.

This is the most robust method because the output is guaranteed to be in a specific, machine-vetted format. It's more complex to set up but is the gold standard for integrating AI into application logic.

Strategy 3: Post-Processing and Parsing (The Cleanup Crew)

Sometimes, despite your best efforts, the AI will still send you messy data. Your application needs to be resilient enough to handle this. This is your last line of defense.

Extract with Regex

A common failure mode is the AI wrapping its perfect JSON in conversational text or a markdown code block. Regular expressions are your best friend here.

For example, to extract a JSON object from a markdown block like {...} , you can use a regex pattern. Here's a JavaScript example:

function extractJson(rawResponse) {
  const jsonMatch = rawResponse.match(/\n([\s\S]*?)\n/);
  if (jsonMatch && jsonMatch[1]) {
    return jsonMatch[1];
  }
  // Fallback for JSON that isn't in a markdown block
  const plainJsonMatch = rawResponse.match(/\{([\s\S]*)\}/);
  if (plainJsonMatch) {
    return plainJsonMatch[0];
  }
  return null;
}

Defensive JSON Parsing

Never assume the extracted string is valid JSON. Always wrap your parsing logic in a try-catch block to handle syntax errors gracefully without crashing your application.

const jsonString = extractJson(aiResponse);
let data;

if (jsonString) {
  try {
    data = JSON.parse(jsonString);
  } catch (error) {
    console.error("Failed to parse JSON:", error);
    // Handle the error: retry, return a default, or notify the user
    data = null;
  }
} else {
  console.error("No JSON object found in the response.");
}

Putting It All Together: A Case Study

Let's build a simple "Product Title Generator". We give it a description, and it gives us a catchy title.

The Naive Approach (Bad):

• Prompt: "Generate a title for a coffee mug that keeps drinks hot for 12 hours."
• Potential Output: "Certainly! A great title for your coffee mug would be 'The Emberstone 12-Hour Thermal Mug'. It emphasizes both its long-lasting heat retention and a premium feel."
• Problem: Our code now has to parse this paragraph to find the title in quotes. It's slow and will break easily.

The Layered, Robust Approach (Good):

1. Prompt Engineering + JSON Mode: We create a sharp, constrained prompt.

   You are a product naming API. Generate 3 catchy title options for the given product description. Your response must be a valid JSON object, and nothing else. The JSON object should have a single key, "titles", which is an array of strings.
   
   Product Description: A coffee mug that keeps drinks hot for 12 hours.

2. Expected API Response:

   {
     "titles": [
       "The 12-Hour HotShot Mug",
       "Everwarm Thermal Travel Mug",
       "Pyro-Lock 12hr Coffee Companion"
     ]
   }

3. Post-Processing Fallback: Our application code first tries to parse the response directly. If that fails, it uses a regex to find a JSON object within the text, and then tries parsing that. If both fail, it logs an error and informs the user.

With this layered approach, we get clean, predictable data 99% of the time, and our application can gracefully handle the 1% of cases where the AI goes off-script.

Conclusion: Finding the Right Balance

Working with LLMs is a dance between instruction and interpretation. While their creativity is a strength, their verbosity can be a significant liability in a production environment. By adopting a multi-layered strategy—starting with sharp prompt engineering, enforcing strict output formats like JSON, and backing it all up with resilient post-processing—you can transform a talkative AI into a disciplined and efficient partner.

This isn't about stifling the AI's capabilities; it's about channeling them. By cleaning up the output, you make your application faster, more reliable, and more cost-effective, allowing you to focus on building amazing features instead of debugging unpredictable strings.