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Very confused. When you enable structured output the response should adhere to the JSON schema EXACTLY, not best effort, by constraining the output via guided decoding. This is even documented in OpenRouter's structured output doc

> The model will respond with a JSON object that strictly follows your schema

Gemini is listed as a model supporting structured output, and yet its fail rate is 0.39% (Gemini 2.0 Flash)!! I get that structured output has a high performance cost but advertising it as supported when in reality it's not is a massive red flag.

Worst yet response healing only fixes JSON syntax error, not schema adherence. This is only mentioned at the end of the article which people are clearly not going to read.

WTF

You're exactly right. The llguidance library [1,2] seems to have emerged as the go-to solution for this by virtue of being >10X faster than its competition. It's work from some past colleagues of mine at Microsoft Research based on theory of (regex) derivatives, which we perviously used to ship a novel kind of regex engine for .NET. It's cool work and AFAIK should ensure full adherence to a JSON grammar.

llguidance is used in vLLM, SGLang, internally at OpenAI and elsewhere. At the same time, I also see a non-trivial JSON error rate from Gemini models in large scale synthetic generations, so perhaps Google hasn't seen the "llight" yet and are using something less principled.

1: https://guidance-ai.github.io/llguidance/llg-go-brrr 2: https://github.com/guidance-ai/llguidance

> Here's something most developers overlook: if an LLM has a 2% JSON defect rate, and Response Healing drops that to 1%, you haven't just made a 1% improvement. You've cut your defects, bugs, and support tickets in half.

If part of my system can't even manage to output JSON reliably, it needs way more "healing" than syntax munging. This comes across as naive.

Plus, that claim isn't even true. A 1% and 2% JSON defect rate are going to annoy a similar amount of people into filing bugs and tickets.

1% fail rate on API requests is horrifyingly embarrassing.

Sounds like we are twice as close to AGI!

"it's not just X, it's Y"

Don't you worry about Planet Express, let me worry about blank.

Model itself can't output JSON reliably. It's on you building a system around the model to make sure it either returns correct output or errors which is trivial to do.

But, but, you've just cut your defects, bugs, and support tickets in half!

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>What about XML? The plugin can heal XML output as well - contact us if you’d like access.

Isn't this exactly how we got weird html parsing logic in the first place, with "autohealing" logic for mismatched closing tags or quotes?

This is probably a bit different. An LLM outputs a token at a time ("autoregressively") by sampling from a per-position token probability distribution, which depends on all the prior context so far. While the post doesn't describe OpenRouter's approach, most structured LLM output works by putting a mask over that distribution, so that any token which would break the intended structure has probability zero and cannot be sampled. So for instance, in the broken example from the post,

    {"name": "Alice", "age": 30

I haven't played much with structured output, but I imagine the biggest risk is that you may force the model to work with contexts outside its training data, leading it to produce garbage, though hopefully syntactically-correct garbage.

I don't understand, though, why the probability of incorrect JSON wouldn't go to 0, under this framework (unless you hit the max sequence length before the JSON ended.) The post implies that JSON errors still happen, so it's possible they're doing something else.

I did some searching for an open-source version of this and found this pretty neat library for Elixir called json_remedy

https://github.com/nshkrdotcom/json_remedy

One of the best shitposts I have ever seen, by far. Absurdism taken to its finest form.

I have built something similar before. But I’ve never had any problems with Gemini not doing Json properly. The problematic models are the open models such as Gemma and GPT OSS.

Dear Openrouter blog authors, could you please stop writing your blogposts with LLMs?

The content of your posts is really insightful and interesting, but it's feel like junk quality because of the way LLMs write blogposts.

What was your prompt?

A lot of it was finger written -- curious which part sounded like LLM to you?

> > Here's something most developers overlook: if an LLM has a 2% JSON defect rate, and Response Healing drops that to 1%, you haven't just made a 1% improvement. You've cut your defects, bugs, and support tickets in half.

This sounds AI written.

Meaning parts were LLM written? With no disclosure?

"With no disclosure?"

Why do you have an expectation that a company will disclose to you when they use AI for their copywriting? Do you want them to disclose the software they used to draft and publish? If a manager reviewed the blog post before it went live?

Using words written by other people without disclosure has always been frowned upon. It's called plagiarism.

Plagiarism is bad for a lot of reasons, all of which also apply to the undisclosed use of generative AI.

Why not just publish the prompt? I can then take an LLM of my taste to reformat it the way I want.

Basically, I'm asking for open source blogging!

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Next up: blog healing

Is this a joke? Am I going crazy?

I don't like this future we're going towards where we have to trick our software (which we can no longer understand the workings of) into doing what we tell it to by asking it nicely, or by putting another black box on the end to "fix" the output. This is the opposite of engineering. This is negotiation with a genie trapped in silicon.

it does seem as if the world has gone insane

we have brilliant machines that can more or less work perfectly

then the scam artists have convinced people that spending a trillion dollar and terawatts to get essentially a biased random number generator to produce unusable garbage is somehow an improvement

These models have turned a bunch of NLP problems that were previously impossible into something trivial. I have personally built extremely reliable systems from the biased random number generator. Our f-score using "classic" NLP went from 20% to 99% using LLMs.

NLP, natural language processing for the unfamiliar. LLMs are tailor made for this work particularly well. They're great tokenizers of structured rules. Its why they're also halfway decent at generating code in some situations.

I think the fall down you see is in logical domains of that rely on relative complexity and contextual awareness in a different way. I've had less luck, for example, having AI systems parse and break down a spreadsheet with complex rules. Thats simply recent memory

It is easier to realize that software development was never engineering. Physical engineering is reliant on physics, while software is reliant on other software. Physics are static and as regarding practical engineering is known and can be applied rigorously and taught in courses. Software is constantly changing, contain tons of edge cases, and as we can see by recent developments, can change in unpredictable ways and lead to entirely new paradigms.

So, the software that you learned on is changing. You aren't going crazy, but the ground is indeed shifting. The problem is that you assumed it couldn't shift because you were applying the wrong constraints.

I thought structured output was done by only allowing tokens that would produce valid output. For their example of a missing closing bracket, the end token wouldn't be allowed, and it would only accept tokens that contain a digit, comma, or closing bracket. I guess that must not be the case, though. Doing that seems like a better way to address this.

That is a way of doing that, but it's quite expensive computationally. There are some companies that can make it feasible [0], but it's often not a perfect process and different inference providers implement it different ways.

[0] https://dottxt.ai/

Out of curiosity, why is it so expensive? Shouldn't constraining the possible result tokens make the inference less expensive? (because you have to calculate less logits and could occasionally even skip tokens entirely if there is only one valid option)

Is there anything in the JSON grammer that only allows one valid option? In any case, I also don't understand why it would be costly. The fact that tokens are typically multiple characters would complicate things somewhat, but checking that a given token results in valid partial JSON doesn't seem too hard.

Freeform JSON not so much I think, but if you combine it with a schema and strict whitespace/formatting rules, you could get quite a few.

I think there are lots of boilerplate sequences like '":{' or '":[' or '", "', etc - though they might already be compressed into a single token if the tokenizer was trained on enough JSON.

There are also situations where the schema would only allow a specific field name as the next token, e.g. if it was the only remaining valid and required field, or if fields have to be output in a specific order.

Good point, I momentarily forgot about the ability to specify a schema. In that case, you'd have a lot of places where there's only one possible output. Even if you have multiple fields and there are no ordering requirements, typical schemas won't take long to get to a unique field name prefix. If you've output `"us` then `ername"` is likely to be the only valid continuation in many cases.

I have used structured outputs both with OpenAI and the Gemini models. In the beginning they had some rough edges but lately it's been smooth sailing.

Seems like Openrouter also supports structured outputs.

https://openrouter.ai/docs/guides/features/structured-output...

How do they know the output needs to be in json format?

This is good, is there a python library to do this ?

This really gets at the heart of my instinctive dislike of how LLMs are being deployed. A core feature of computers, and tools in general, is reliability. I like software because you can set something up, run it, and (ideally) know that it will do the same job the same way each subsequent time you run it. I want a button that is clearly labeled, and when pressed, does a specific thing, acting like a limb, an extension of my will. I do not, in almost all cases, want my computer to be another distinct entity that I conduct social interactions with.

Maybe people got used to computers being unreliable and unpredictable as the UIs we shipped became more distracting, less learnable, always shifting and hiding information, popping up suggestions and displaying non-deterministic-seeming behavior. We trained users to treat their devices like unruly animals that they can never quite trust. So now the idea of a machine that embodies a more clever (but still unreliable) animal to wrangle sounds like a clear upgrade.

But as someone who's spent an inordinate amount of time tweaking and tuning his computing environment to prune out flakey components and fine-tune bindings and navigation, the idea of integrating a tool into my workflow that does amazing things but fails utterly even 1% of the time sounds like a nightmare, a sort of perpetual torture of low-grade anxiety.

> We trained users to treat their devices like unruly animals that they can never quite trust. So now the idea of a machine that embodies a more clever (but still unreliable) animal to wrangle sounds like a clear upgrade.

I wish I didn't agree with this, but I think you're exactly right. Even engineers dealing with systems we know are deterministic will joke about making the right sacrifices to the tech gods to get such-and-such working. Take that a step further and maybe it doesn't feel too bad to some people for the system to actually not be deterministic, if you have a way to "convince" it to do what you want. How depressing.

Software is only deterministic if the software it relies on never changes. Forced updates make this impossible, so treating software as deterministic is actually wrong.

This is incredible!