Slashing LLM Latency by 80%: Using OpenAI Predicted Outputs in Spring AI

In 2026, treating document and code refactoring as cold greenfield generation is pure architectural negligence. OpenAI's Predicted Outputs feature slashes response latency by up to 80% via speculative decoding, yet most Java teams are still leaving users staring at progress spinners for identical boilerplate.

Why Most Developers Get This Wrong

Treating updates as fresh completions: Passing an entire 500-line class to gpt-4o and asking for an updated method forces the model to generate every single unchanged token from scratch.

Ignoring speculative rejection penalties: Blindly sending massive, inaccurate prediction payloads without dynamic scoping causes token verification misses, which actually degrades latency instead of improving it.

Using naive generic abstraction layers: Sticking to high-level LLM wrappers that lack support for provider-specific parameters like prediction , completely missing out on speculative decoding…