Structuring AI Output for Production Systems

By Frederick Lowe, Mar 10, 2025

For the past two years, I've served as the Lead Architect (and frequent bench engineer) on several applied projects integrating various Large Language Models (LLMs).

Every project has the goal of generating or extracting programmatically reliable outputs for use with existing enterprise publishing, CRM, messaging, and scheduling systems. There is an unlimited appetite for systems that do this reliably.

Thankfully, producing consistent outputs from LLMs has gotten easier as each new generation improves on the last. But LLMs still hallucinate, misinterpret prompts, and commit basic errors and omissions.

Techniques that mitigate these deficiencies improve performance:

• RAG (Retrieval-Augmented Generation): requires engineering to architect retrieval systems, maintain source hygiene, and debug multi-component failures
• Custom GPTs: require engineering to configure platform-specific implementations and manage vendor dependencies
• Fine Tuning: requires engineering to prepare training data, manage training runs, and maintain model versions

But when domain experts who understand output requirements can't implement solutions without engineering intermediaries, these approaches create bottlenecks.

In this article, I'll share a vendor-independent method I use in production for programmatically reliable generation and extraction: JSON Meta-Prompting (JMP).

JMP's primary advantage: domain experts can implement and iterate on AI features without engineering intermediaries.

How JSON Meta-Prompting Works

JSON Meta-prompting (as I practice it) involves embedding in-situ prompts at the field level. Each prompt is direct and comprehensive. The style features repetitive requests (Replace this field with...) that instruct completion of each field in a specific way.

Here's a simplified prompt example for a publishing application:

{
  "Summary": "Replace this field with a 128–196 word summary explaining how to visit art galleries in or around {%cityName%}.",
  "Location": "{%cityName%}",
  "Attractions": [
    {
      "Name": "Replace this field with the name of the art gallery.",
      "Link": "Replace this field with a link to the art gallery's official website. If no official site exists, use the most authoritative source available (museum directory, tourism site, etc.).",
      "Description": "Replace this field with a 96–128 word description of the art gallery.",
      "Fun Fact": "Replace this field with a 48–96 word fun fact about the art gallery. Fun facts should be historical, cultural, or location-related. Only include verifiable, interesting facts. If no compelling fact exists, omit this field entirely."
    }
  ],
  "Getting Here": "Replace this field with a 96–128 word paragraph describing how to reach these galleries within the city.",
  "Closing": "Close the article by summarizing key points. Avoid word-for-word redundancy with previous sections."
}

Why JSON Meta-Prompting Is Ideal For Production

• Zero Ambiguity: The LLM either returns valid JSON or fails completely. For most frontier models, valid JSON is virtually guaranteed , and in the rare case of failure, the work unit can simply be re-rolled (within retry limits).
• Portable Prompts: Prompts run unchanged across vendors and models, avoiding vendor lock-in and enabling seamless load-balancing or fallback processing.
• Consistent Scaling: Explicit field-level instructions minimize output variation, reducing the need for manual review or complex downstream edge-case handling.

For production projects, the combined benefits of simplicity, reliability, and portability outweigh the verbose prompt structure.

Takeaway

JSON Meta-Prompting produces outputs that are easy to validate and refine , either through additional LLM passes (@see: prompt chaining) or standard programmatic validation.

Because it aligns naturally with the JSON-based ecosystem of REST APIs, it integrates cleanly into existing production pipelines.

Alternative approaches introduce lock-in or parsing ambiguities that compound at scale.