To me, it's a criminally underused tool. While "raw" LLMs are cool, they're annoying to use as anything but chatbots, as their output is unpredictable and basically impossible to parse programmatically.
Structured outputs solve that problem neatly. In a way, they're "neural networks without the training". They can be used to solve similar problems as traditional neural networks, things like image classification or extracting information from messy text, but all they require is a Zod or Pydantic type definition and a prompt. No renting GPUs, labeling data and tuning hyperparameters necessary.
They often also improve LLM performance significantly. Imagine you're trying to extract calories per 100g of product, but some product give you calories per serving and a serving size, calories per pound etc. The naive way to do this is a prompt like "give me calories per 100g", but that forces the LLM to do arithmetic, and LLMs are bad at arithmetic. With structured outputs, you just give it the fifteen different formats that you expect to see as alternatives, and use some simple Python to turn them all into calories per 100g on the backend side.
One way teams exploit that - force LLM to go through a predefined task-specific checklist before answering. This custom hard-coded chain of thought boosts the accuracy and makes reasoning more auditable.
I was trying to make a decent cocktail recipe database, and scraped the text of cocktails from about 1400 webpages. Note that this was just the text of the cocktail recipe, and cocktail recipes are comparatively small. I sent the text to an LLM for JSON structuring, and the LLM routinely miscategorized liquor types. It also failed to normalize measurements with explicit instructions and the temperature set to zero. I gave up.
the idea is that instead of using JSON.parse, we create a custom Type.parse for each type you define.
so if you want a:
class Job { company: string[] }
{ "company": "Amazon" }
https://www.boundaryml.com/blog/schema-aligned-parsing
and since its only post processing, the technique will work on every model :)
for example, on BFCL benchmarks, we got SAP + GPT3.5 to beat out GPT4o ( https://www.boundaryml.com/blog/sota-function-calling )
computers -> assembly -> HLL -> web -> cloud -> AI
Nothing on that list has disappeared, but the work has changed enough to warrant a few major versions imo.
V1.0: describing solutions to specific problems directly, precisely, for machines to execute.
V2.0: giving machine examples of good and bad answers to specific problems we don't know how to describe precisely, for machine to generalize from and solve such indirectly specified problem.
V3.0: telling machine what to do in plain language, for it to figure out and solve.
V2 was coded in V1 style, as a solution to problem of "build a tool that can solve problems defined as examples". V3 was created by feeding everything and the kitchen sink into V2 at the same time, so it learns to solve the problem of being general-purpose tool.
My Hail Mary is it’s going to be groups of machines gathering real world data, creating their own protocols or forms of language isolated to their own systems in order to optimize that particular system’s workflow and data storage.
Exactly what I felt. Semver like naming analogies bring their own set of implicit meanings, like major versions having to necessarily supersede or replace the previous version, that is, it doesn't account for coexistence further than planning migration paths. This expectation however doesn't correspond with the rest of the talk, so I thought I might point it out. Thanks for taking the time to reply!
1) it is a breaking change from the prior version
2) it is an improvement in that, in its ideal/ultimate form, it is a full superset of capabilities of the previous version