How JSON Mode Kicked Off the AI Transformation — From Chatbots to Agents
Why structured outputs from LLMs really changed the game
Back in 2023, extracting reliable JSON from large language models like GPT-3.5 was a pain. Outputs were often inconsistent—missing brackets, unclosed tags, unescaped characters, or malformed structures that required clunky workarounds like regex or recursive model calls to enforce self-correction. While effective, these methods highlighted a critical gap: for AI to truly integrate into our systems, it needed to produce structured, machine-readable data consistently.
Today, Structured Outputs mode is the backbone powering the next wave of building software. By enabling LLMs to generate precise, schema-compliant outputs, JSON mode has empowered Engineers, data analysts, and businesses to embed AI directly into their operational workflows. For example:
Automated data pipelines
Autonomous coding agents
Information extraction from unstructured documents
Real-time moderation and analytics
Automated report generation and summarisation
and more.
As a result, structured outputs have made AI a dependable component of production systems. Large language models (LLMs) can now function as back-end components in data-centric systems, rather than merely serving as front-end conversational agents. This shift highlights an important realisation: AI’s real value lies in seamless system integration and automation, not merely human-like interaction.
If leveraged responsibly, this deep integration will elevate human productivity to levels never seen before, potentially phasing out certain aspects of the current Economy while giving rise to new opportunities.
According to McKinsey (2023), generative AI could add up to $4.4 trillion annually to the global economy. The real challenge, however, is ensuring this value isn’t disproportionately captured by a few players but distributed equitably across regions, industries, and communities.
Curious about where to start with structured outputs? See links below:
How can we advance evaluation methods to make AI outputs more transparent and trustworthy?