Reshare — content by Julien Mutzenhardt
€17,000 in penalties for a badly formatted JSON: the fatal mistake with LLMs!
A developer caused a massive delay in the processing of crucial files for his company. The reason? A simple integration error. 
Yet he had clearly specified in his prompt: “Respond in JSON.” The problem is that the model, very politely, responded: “Of course, here is the information:”, before inserting its code.
Result: the parser, which was expecting a raw JSON object, crashed in a loop. 
The lesson: A prompt is only an instruction, not an obligation.
It’s like telling your dog not to touch the chicken on the table, then going to bed. Nothing actually forces the artificial intelligence model to strictly obey the requested format.
The real solution: “Constrained Decoding”
Rather than hoping the model complies with your text instructions, give it a “JSON Schema” (the exact blueprint of the expected response, to define for example that an amount must be a number).
How does it work under the hood?
Instead of letting the LLM choose the next word from its entire vocabulary, the generation engine recalculates the allowed “tokens” in real time. For example, to start a JSON object, it will only allow the opening brace {. The model has no other choice. The format thus becomes valid by construction, which avoids launching thousands of unnecessary new attempts (retries)! 
How to implement it without recoding an overly complex monster?
Simply send your JSON Schema to the standard APIs (like OpenAI or Anthropic), which take care of processing it. And if you host your own AI model locally, engines like vLLM handle it just as easily.
Stop doing whatever in production and secure the output data from your LLMs! 
The summary of this article was generated by AI.
And you, have you ever seen a system blow up mid-flight because of an unexpected response from an artificial intelligence?
#ArtificialIntelligence #Development #JSON #LLM #OpenAI #VLLM #Programming #Tech
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LinkedIn Sources
- Profile: Pierre Jean Duvivier dit Sage (LinkedIn)
- Shared author: Julien Mutzenhardt (LinkedIn)
The post 🚨 €17,000 in penalties for a badly formatted JSON: the fatal error with LLMs! 🚨 appeared first on les plumes qui mordent.
Frequently Asked Questions
FAQs 17000 in Penalties for a Badly Formatted JSON The Fatal Mistake with LLMs
1 What is this 17000 penalty about
It refers to a realworld case where an AI system produced malformed JSON output which broke a downstream process and led to a financial penalty of 17000 Its used as a cautionary example of why output formatting matters
2 What is JSON
JSON is a simple text format for structuring data as keyvalue pairs lists and nested objects Its the standard way software exchanges data
3 What does badly formatted JSON mean
JSON that breaks the syntax rules for example missing quotes trailing commas unescaped characters unbalanced brackets or extra text beforeafter the object Parsers reject it
4 Why do LLMs produce invalid JSON
LLMs generate text probabilistically They may add explanations use markdown code fences hallucinate fields truncate output or slightly deviate from the schema especially without strict instructions or validation
5 How can a formatting error cost 17000
If a system blindly trusts LLM output invalid JSON can crash automated pipelines cause wrong transactions trigger contractSLA penalties or force manual fixes One small syntax error can cascade into real financial loss
6 Whats the most common JSON mistake LLMs make
Wrapping the JSON in markdown or adding conversational text around it Other frequent issues trailing commas single quotes and truncated output
7 How do I get an LLM to return valid JSON
Ask explicitly for JSON only no explanation provide a schema or example use low temperature and most importantly validate and retry on failure
8 What is JSON schema validation
A way to check that JSON matches an expected structure Tools like JSON Schema validators catch errors before they reach production
9 Should I trust LLM output without checking it
No Always validate LLM output programmatically Treat it as untrusted input just like usersubmitted data
10 What are structured outputs or JSON mode in LLMs
Features offered by some model providers that constrain
