I'm not 100% convinced by this post. I'd like to see a more extensive formal eval that demonstrates that structured outputs from different providers reduces the quality of data extraction results.
Assuming this holds up, I wonder if a good workaround for this problem - the problem that turning on structured outputs makes errors more likely - would be to do this:
1. Prompt the LLM "extract numbers from this receipt, return data in this JSON format: ..." - without using the structured output mechanism.
2. If the returned JSON does indeed fit the schema then great, you're finished! But if it doesn't...
3. Round-trip the response from the previous call through the LLM again, this time with structured outputs configured. This should give you back the higher quality extracted data in the exact format you want.
that workaround we've found works quite well, but the problem is that its not sufficient to just retry in the case of failed schema matches (its both inefficient and also imo incorrect).
Take these two scenarios for example:
Scenario 1. My system is designed to output receipts, but the user does something malicious and gives me an invoice. during step 2, it fails to fit the schema, but then you try with step 3, and now you have a receipt! Its close, but your business logic is not expecting that. Often when schema alignment fails, its usually because the schema was ambiguous or the input was not valid.
Scenario 2. I ask the LLM to produce this schema:
class Person {
name string
past_jobs string[]
}
However the person only has ever worked at 1 job. so the LLM outputs: { "name": "Vaibhav", "past_jobs": "Google" }. Technically since you know you expect an array, you could just transform the string -> string[].
(author here) To be more specific, here's a benchmark that we ran last year, where we compared schema-aligned parsing against constrained decoding (then called "Function Calling (Strict)", the orange ƒ): https://boundaryml.com/blog/sota-function-calling
I wonder what it would look if you redid the benchmarks, testing against models that have reasoning effort set to various values. Maybe structured output is only worse if the model isn't allowed to do reasoning first?
Isn't it better to put it in an agent loop, with the structured output json just specified as a tool? The function call can then just return a summary of the parsed input. We can add in the system prompt a validation step to ask the llm to verify it has provided inputs correctly. This will allow the llm itself to self reflect and correct if needed.
I understand this but A) then they should have done it here B) the idea that you can't get CoT x JSON without sacrificing JSON formatting is flat out wrong with ~any 2025 model. (i.e. reasoning models and their APIs specifically enable this)
Just a week ago, I rewrote our RAG pipeline to use structured outputs, and the tests showed no significant difference in quality after a few tweaks (under vLLM). What helped was that we have a pipeline where another LLM automatically scores 'question-expected answer' pairs, so what we did was: tweak the schema/prompt => evaluate => tweak again, until we got good results in most cases, just like with free-form prompts.
Several issues were found:
1. A model may sometimes get stuck generating whitespace at the end forever (the JSON schema allows it), which can lock up the entire vLLM instance. The solution was to use xgrammer, because it has a handy feature that disallows whitespace outside of strings.
2. In some cases I had to fiddle with metainformation like minItems/maxItems for arrays, or the model would either hallucinate or refuse to generate anything.
3. Inference engines may reorder the fields during generation, which can impact the quality due to the autoregressive nature of LLMs (like, the "calculation" field must come before the "result" field). Make sure the fields are not reordered.
4. Field names must be as descriptive as possible, to guide the model to generate expected data in the expected form. For example, "durationInMilliseconds" instead of just "duration".
Basically, you can't expect a model to give you good results out of the box with structured outputs if the schema is poorly designed or underspecified.
Yup. I instantly linked these because the multiple papers who claim structured outputs harm quality are not just wrong, but fatally damaging to the whole AI ecosystem especially AI agents.
There are places where structured outputs harms creativity, but usually that's a decoding time problem which is similarly solved with better sampling, like they talk about in this paper: https://arxiv.org/abs/2410.01103
Claims of harmed reasoning performance are really evidence that 1. Your structured generation backend is bad or 2. Some shenanigans/interactions with temperature/samplers (this is the most common by far) or 3. You are bad at benchmarking.
If your output schema doesn’t capture all correct outputs, that’s a problem with your schema, not the LLM. A human using a data entry tool would run into the wrong issue. Letting the LLM output whatever it wants just makes it so you have to deal with ambiguities manually, instead of teaching the LLM what to do.
I usually start by adding an error type that will be overused by the LLM, and use that to gain visibility into the types of ambiguities that come up in real-world data. Then over time you can build a more correct schema and better prompts that help the LLM deal with ambiguities the way you want it to.
Also, a lot of the chain of thought issues are solved by using a reasoning model (which allows chain of thought that isn’t included in the output) or by using an agentic loop with a tool call to return output.
establishment_name: str
date: str = Field(description="YYYY-MM-DD")
total: float = Field(description="The total amount of the receipt")
currency: str = Field(description="The currency used for everything on the receipt")
items: list[Item] = Field(description="The items on the receipt")
</code>
There needs to be a better evaluation and a better provided schema that captures the full details of what is expected to be captured.
> What kind of error should it return if there's no total listed on the receipt? Should it even return an error or is it OK for it to return total = null?
Additionally, the schema allows optional fields, so the LLM is free to skip missing fields if they are specified as such.
The very first example, which is held up as an error, is actually arguably correct. If you asked a human (me) how many bananas were purchased, they clearly purchased one banana.
Yes the banana weighs 0.4 pounds. But the question was not to return the weight or the quantity, the question was to return the quantity.
It seems like more instructions are needed in the prompt that the author is not even aware of.
A very common peeled banana weight is 100g (“metric banana”). This is convenient for calorie counting.
0.4lbs for a single banana as the peeled weight is probably around 125g.
I used Python's Instructor[1], a package to force the model output to match the predefined Pydantic model.
It's used like in the example below, and the output is guaranteed to fit the model.
import instructor
from pydantic import BaseModel
class Person(BaseModel):
name: str
age: int
client = instructor.from_provider("openai/gpt-5-nano")
person = client.create(
response_model=Person,
messages=[{"role": "user", "content": "Extract: John is a 30-year-old"}]
)
print(person)
I defined a response model for chain of thought prompt with answers and its thinking process, then asked questions.
class MathAnswer(BaseModel):
value: int
reasoning: str
answer = client.create(
response_model=MathAnswer,
messages=[{"role": "user", "content": "What's the answer to 17*4+1? Think step by step"}]
)
print(f"answer={answer.value}, {answer.reasoning}")
This worked in most cases, but once in a while, it produced very strange results:
67, First I calculated 17*4=68, then I added 1 so the answer is 69
The actual implementation was much more complicated with many and complex proerties, a lot of inserted context, and long, engineered prompt, and it happened only a few times, so I took hours to figure out if it's caused by a programming bug or just LLM's randomness.
Turned out, because I defined MathAnswer in that order, the model output was in the same order and it put the `reasoning` after the `answer`, so the thinking process didn't influence the answer like `{"answer": 67, "reasoning": "..."}` instead of `{"reasoning": "...", "answer": 69}`.
I just changed the order of the model's properties and the problem was gone.
class MathAnswer(BaseModel):
reasoning: str
value: int
ETA: Codex and Claude Code only said how shit my prompt and RAG system were, then suggested how to improve them, but it only made the problem worse. They really don't know how they work.
I don't really understand the point around error handling. Sure, with structured outputs you need to be explicit about what errors you're handling and how you're handling them. But if you ask the model to return pure text, you now have a universe of possible errors that you still need to handle explicitly (you're using structured outputs, so your LLM response is presumably being consumed programmatically?), including a whole bunch of new errors that structured outputs help you avoid.
Also, meta gripe: this article felt like a total bait-and-switch in that it only became clear that it was promoting a product right at the end.
While I agree that you must be careful when using structured outputs, the article doesn't provide good arguments:
1. In the examples provided, the author compares freeform CoT + JSON output vs. non-CoT structured output. This is unfair and biases the results towards what they wanted to show. These days, you don't need to include a "reasoning" field in the schema as mentioned in the article; you can just use thinking tokens (e.g., reasoning_effort for OpenAI models). You get the best of both worlds: freeform reasoning and structured output. I tested this, and the results were very similar for both.
3. There's no silver bullet. Structured outputs might improve or worsen your results depending on the use case. What you really need to do is run your evals and make a decision based on the data.
BTW, the structured outputs debate is significantly more complicated than even your own post implies.
You aren't testing structured outputs+model alone, you are testing
1. The structured outputs backend used. There are at least 3 major free ones, outlines, xgrammer, lm-format-enforcer and guidance. OpenAI, Anthropic, Google, and Grok will all have different ones. They all do things SIGNIFICANTLY differently. That's at least 8 different backends to compare.
2. The settings used for each structured output backend. Oh, you didn't know that there's often 5+ settings related to how they handle subtle stuff like whitespaces? Better learn to figure out what these settings do and how to tweak them!
3. The models underlying sampling settings, i.e. any default temperature, top_p/top_k, etc going on. Remember that the ORDER of application of samplers matters here! Huggingface transformers and vLLM have opposite defaults on if temperature happens before sampling or after!
4. The model, and don't forget about differences around quants/variants of the model!
Almost no one who does any kinds of these analysis even talk about these additional factors, including academics.
Sometimes it feels like I'm the only one in this world who actually uses this feature at the extremes of its capabilities.
Does anyone have more benchmarks or evals with data on this topic? The claimed 20% accuracy reduction is significant.
Structured output was one of the lesser known topics that AI consultants and course writers got a lot of mileage out of because it felt like magic. A lot of management people would use ChatGPT but didn’t know how to bridge the text output into a familiar API format, so using a trick to turn it into JSON felt like the missing link. Now that I think about it, I don’t recall seeing any content actually evaluating the impact of constrained output on quality though.
This blog post blurs the lines between output quality reduction and incorrect error handling, though. I’d like to see some more thorough benchmarking that doesn’t try to include obvious schema issues in the quality reduction measurements.
I really like BAML but this post seems a little too much like a BAML funnel. Here are three methods that worked for me consistently since constrained sampling first came out:
1. Add a validation step (using a mini model) right at the beginning - sub-second response times; the validation will either emit True/False or emit a function call
2. Use a sequence of (1) large model without structured outputs for reasoning/parsing, chained to (2) small model for constrained sampling/structured output
3. Keep your Pydantic models/schemas as flat (not too nested and not too many enumarations) and "help" the model in the system prompt as much as you can
I have heard this argument before, but never actually seen concrete evals.
The argument goes that because we are intentionally constraining the model - I believe OAI’s method is a soft max (I think, rusty on my ML math) to get tokens sorted by probability then taking the first that aligns with the current state machine - we get less creativity.
Maybe, but a one-off vibes example is hardly proof. I still use structured output regularly.
Oh, and tool calling is almost certainly implemented atop structured output. After all, it’s forcing the model to respond with a JSON schema representing the tool arguments. I struggle to believe that this is adequate for tool calling but inadequate for general purpose use.
The team behind the Outlines library has produced several sets of evals and repeatedly shown the opposite: that constrained decoding improves model performance (including examples of "CoT" which the post claims isn't possible). [0,1]
There was a paper that claimed constrained decoding hurt performance, but it had some fundamental errors which they also wrote about [2].
People get weirdly superstitious when it comes to constrained decoding as though t somehow "limiting the model" when it's just a simple as applying a conditional probably distribution to the logits. I also suspect this post is largely to justify the fact that BAML parses the results (since the post is written by them).
To be fair, there is "real harm" from constraining LLM outputs related to, for example, forcing lipograms or the letter "E" and a model responding with misspellings of words (deleted E) rather than words that don't actually have the letter "E" at all. This is why some authors propose special decoders to fix that diversity problem. See this paper and most of what it cites around it for examples of this: https://arxiv.org/abs/2410.01103
This is independent from a "quality" or "reasoning" problem which simply does not exist/happen when using structured generation.
Edit (to respond):
I am claiming that there is no harm to reasoning, not claiming that CoT reasoning before structured generation isn't happening.
> "reasoning" problem which simply does not exist/happen when using structured generation
The first article demonstrates exactly how to implement structured generation with CoT. Do you mean “reasoning” other than traditional CoT (like DeepSeek)? I’ll have to look for an reference but I recall the Outlines team also handling this latter case.
It seems like this could be solved by partial structured output, where the structure of the JSON itself is constrained, but the values of the JSON entries is not (so even if "quantity" here is set to int, the model can output "52.2"). Of course, we would need additional parsing, but I think it's a fair compromise.
And about structured outputs messing with chain-of-thought... Is CoT really used with normal models nowadays? I think that if you need CoT you might as well use a reasoning model, and that solves the problem.
From what I have found text -> structured text works well. I do a lot of call center based projects where I need to get intents (what API I need to call to fulfill the user’s request) and add slots (the variable part of the message like addresses).
Even Amazon’s cheapest and fastest model does that well - Nova Lite.
But even without using his framework, he did give me an obvious in hindsight method of handling image understanding.
I should have used a more advanced model to describe the image as free text and then used a cheap model to convert text to JSON.
I also had the problem that my process hallucinated that it understood the “image” contained in a Mac .DS_Store file
OpenAI structured outputs are pretty stable for me. Gemini sometimes responds with a completely different structure.
Gemini 3 flash with grounding sometimes returns json inside ```json...``` causing parsing errors.
> Chain-of-thought is crippled by structured outputs
I don't know if this is true. Libraries such as Pydantic AI and I would assume the model provider SDKs stream different events. If COT is needed then a <think> section would be emitted and then later the structured response would occur when the model begins its final response.
Structured outputs can be quite reliable if used correctly. For example, I designed an AST structure that allows me to reliably generate SQL. The model has tools to inspect data-points, view their value distributions (quartiles, medians, etc). Then once I get the AST structure back I can perform semantic validation easily (just walk the tree like a compiler). Once semantic validation passes (or forces a re-prompt with the error), I can just walk the tree again to generate SQL. This helps me reliably generate SQL where I know it won't fail during execution, and have a lot of control over what data-points are used together, and ensuring valid values are used for them.
I think the trick is just generating the right schema to model your problem, and understanding the depth of an answer that might come back.
Structured outputs work well depending on the tasks. The example mentioned in the blog post output doesn’t say anything because we are missing the prompt/schema definition. Also quantity is quite ambiguous because it could be bananas as a term is readable once on the receipt.
I would love some more detailed and reproducible examples, because the claims don’t make sense for all use cases I had.
It’s not surprising that there could be a very slight quality drop off for making the model return its answer in a constrained way. You’re essentially forcing the model to express the actual answer it wants to express in a constrained language.
However I would say two things:
1. I doubt this quality drop couldn’t be mitigated by first letting the model answer in its regular language and then doing a second constrained step to convert that into structured outputs.
2. For the smaller models I have seen instances where the constrained sampling of structured outputs actually HELPS with output quality. If you can sufficiently encode information in the structure of the output it can help the model. It can effectively let you encode simple branching mechanisms to execute at sample time
> You’re essentially forcing the model to express the actual answer it wants to express in a constrained language.
You surely aren't implying that the model is sentient or has any "desire" to give an answer, right?
And how is that different from prompting in general? Isn't using english already a constraint? And isn't that what it is designed for, to work with prompts that provide limits in which to determine the output text? Like there is no "real" answer that you supress by changing your prompt.
So I don't think its a plausible explanation to say this happens because we are "making" the model return its answerr in a "constrained language" at all.
> You surely aren't implying that the model is sentient or has any "desire" to give an answer, right?
The model is a probabilistic machine that was trained to generate completions and then fine tuned to generate chat style interactions. There is an output, given the prompt and weights, that is most likely under the model. That’s what one could call the model’s “desired” answer if you want to anthropomorphize.
When you constrain which tokens can be sampled at a given timestep you by definition diverge from that
A 3rd alternative is to use the best of both worlds. Have the model respond in free-form. Then use that response + structured output APIs to ask it for json. More expensive, but better overall results. (and you can cross-check between your heuristic parsing vs. the structured output, and retry / alert on miss-matches)
I am doing this with good success parsing receipts with ministral3:14b. The first prompt describes the data being sought, and asks for it to be put at the end of the response. The format tends to vary between json, bulleted lists, and name: value pairs. I was never able to find a good way to get just JSON.
The second pass is configured for structured output via guided decoding, and is asked to just put the field values from the analyzer's response into JSON fitting a specified schema.
I have processed several hundred receipts this way with very high accuracy; 99.7% of extracted fields are correct. Unfortunately it still needs human review because I can't seem to get a VLM to see the errors in the very few examples that have errors. But this setup does save a lot of time.
I would like to see a real example, the one given is assuming wanting float and assigning int.
What if you put “float” instead of int to get the required number?
Also the post is missing another use case, enums in structured data. I’ve been using it successfully for a few months now and it’s doing a fantastic job.
Interesting read and perspective. I had very good results with structured outputs, both text, images and tool calling. Also a lot of SDKs are using it, including Vercel AI SDK.
They worked fine for me. Keep working at it until results are positive instead of rabbit holing into a failure mode with a blog post.
It's usually more productive to right about how LLMs work rather than how they don't. In this case especially, there's improvements that can be made to the schema, without forfeiting on the idea of schemas altogether
Doesn't the Claude APIs recently introduced ability to combine extended thinking with structured outputs overcome this issue? You get the unconstrained(ish) generation in the extended thinking blocks and then structured formatting informed by that thinking in the final output.
The blog post is doubly bad because any "failures" involving images and image understanding can't necessarily be traced back to structured generation at all!!!
Sounds like a you problem. I’m all for people investigating the boundaries of model capability - if you take that as a personal attack, you’re going to have a bad time over the next few years.
While I suspect "hot take" is apt, this wasn't exactly anti-AI. Rather, the author is advocating for their particular way of doing genAI output parsing (as opposed to constrained decoding structured output, they advocate unconstrained decoding with a permissive parsing framework)
I'm not 100% convinced by this post. I'd like to see a more extensive formal eval that demonstrates that structured outputs from different providers reduces the quality of data extraction results.
Assuming this holds up, I wonder if a good workaround for this problem - the problem that turning on structured outputs makes errors more likely - would be to do this:
1. Prompt the LLM "extract numbers from this receipt, return data in this JSON format: ..." - without using the structured output mechanism.
2. If the returned JSON does indeed fit the schema then great, you're finished! But if it doesn't...
3. Round-trip the response from the previous call through the LLM again, this time with structured outputs configured. This should give you back the higher quality extracted data in the exact format you want.
(on of the creators of BAML here) yep! exactly!
that workaround we've found works quite well, but the problem is that its not sufficient to just retry in the case of failed schema matches (its both inefficient and also imo incorrect).
Take these two scenarios for example:
Scenario 1. My system is designed to output receipts, but the user does something malicious and gives me an invoice. during step 2, it fails to fit the schema, but then you try with step 3, and now you have a receipt! Its close, but your business logic is not expecting that. Often when schema alignment fails, its usually because the schema was ambiguous or the input was not valid.
Scenario 2. I ask the LLM to produce this schema:
However the person only has ever worked at 1 job. so the LLM outputs: { "name": "Vaibhav", "past_jobs": "Google" }. Technically since you know you expect an array, you could just transform the string -> string[].thats the algorithm we created: schema-aligned parsing. More here if you're interested: https://boundaryml.com/blog/schema-aligned-parsing
Benchmark wise, when we tested last, it seems to help on top of every model (especially the smaller ones) https://www.reddit.com/r/LocalLLaMA/comments/1esd9xc/beating...
Hope this helps with some of the ambiguities in the post :)
(author here) To be more specific, here's a benchmark that we ran last year, where we compared schema-aligned parsing against constrained decoding (then called "Function Calling (Strict)", the orange ƒ): https://boundaryml.com/blog/sota-function-calling
I wonder what it would look if you redid the benchmarks, testing against models that have reasoning effort set to various values. Maybe structured output is only worse if the model isn't allowed to do reasoning first?
Isn't it better to put it in an agent loop, with the structured output json just specified as a tool? The function call can then just return a summary of the parsed input. We can add in the system prompt a validation step to ask the llm to verify it has provided inputs correctly. This will allow the llm itself to self reflect and correct if needed.
That is more or less what BAML does
I understand this but A) then they should have done it here B) the idea that you can't get CoT x JSON without sacrificing JSON formatting is flat out wrong with ~any 2025 model. (i.e. reasoning models and their APIs specifically enable this)
Just a week ago, I rewrote our RAG pipeline to use structured outputs, and the tests showed no significant difference in quality after a few tweaks (under vLLM). What helped was that we have a pipeline where another LLM automatically scores 'question-expected answer' pairs, so what we did was: tweak the schema/prompt => evaluate => tweak again, until we got good results in most cases, just like with free-form prompts.
Several issues were found:
1. A model may sometimes get stuck generating whitespace at the end forever (the JSON schema allows it), which can lock up the entire vLLM instance. The solution was to use xgrammer, because it has a handy feature that disallows whitespace outside of strings.
2. In some cases I had to fiddle with metainformation like minItems/maxItems for arrays, or the model would either hallucinate or refuse to generate anything.
3. Inference engines may reorder the fields during generation, which can impact the quality due to the autoregressive nature of LLMs (like, the "calculation" field must come before the "result" field). Make sure the fields are not reordered.
4. Field names must be as descriptive as possible, to guide the model to generate expected data in the expected form. For example, "durationInMilliseconds" instead of just "duration".
Basically, you can't expect a model to give you good results out of the box with structured outputs if the schema is poorly designed or underspecified.
Interesting! .TXT has the opposite conclusion, that structured output improves performance:
https://blog.dottxt.ai/say-what-you-mean.html
https://blog.dottxt.ai/prompt-efficiency.html
This also matches my own experiences.
Same for me. Using structured output was much better than without.
Yup. I instantly linked these because the multiple papers who claim structured outputs harm quality are not just wrong, but fatally damaging to the whole AI ecosystem especially AI agents.
There are places where structured outputs harms creativity, but usually that's a decoding time problem which is similarly solved with better sampling, like they talk about in this paper: https://arxiv.org/abs/2410.01103
Claims of harmed reasoning performance are really evidence that 1. Your structured generation backend is bad or 2. Some shenanigans/interactions with temperature/samplers (this is the most common by far) or 3. You are bad at benchmarking.
If your output schema doesn’t capture all correct outputs, that’s a problem with your schema, not the LLM. A human using a data entry tool would run into the wrong issue. Letting the LLM output whatever it wants just makes it so you have to deal with ambiguities manually, instead of teaching the LLM what to do.
I usually start by adding an error type that will be overused by the LLM, and use that to gain visibility into the types of ambiguities that come up in real-world data. Then over time you can build a more correct schema and better prompts that help the LLM deal with ambiguities the way you want it to.
Also, a lot of the chain of thought issues are solved by using a reasoning model (which allows chain of thought that isn’t included in the output) or by using an agentic loop with a tool call to return output.
This ^^^^
While the provided schema has a "quantity" field, it doesn't mention the units.
<code>
class Item(BaseModel):
class Receipt(BaseModel): </code>There needs to be a better evaluation and a better provided schema that captures the full details of what is expected to be captured.
> What kind of error should it return if there's no total listed on the receipt? Should it even return an error or is it OK for it to return total = null?
Additionally, the schema allows optional fields, so the LLM is free to skip missing fields if they are specified as such.
The very first example, which is held up as an error, is actually arguably correct. If you asked a human (me) how many bananas were purchased, they clearly purchased one banana.
Yes the banana weighs 0.4 pounds. But the question was not to return the weight or the quantity, the question was to return the quantity.
It seems like more instructions are needed in the prompt that the author is not even aware of.
Or one batch of bananas, weighing 0.4 pounds. The number of bananas is not specified in the receipt.
A very common peeled banana weight is 100g (“metric banana”). This is convenient for calorie counting. 0.4lbs for a single banana as the peeled weight is probably around 125g.
https://www.reddit.com/r/dataisbeautiful/comments/bs741l/oc_...
Story time.
I used Python's Instructor[1], a package to force the model output to match the predefined Pydantic model. It's used like in the example below, and the output is guaranteed to fit the model.
I defined a response model for chain of thought prompt with answers and its thinking process, then asked questions. This worked in most cases, but once in a while, it produced very strange results: The actual implementation was much more complicated with many and complex proerties, a lot of inserted context, and long, engineered prompt, and it happened only a few times, so I took hours to figure out if it's caused by a programming bug or just LLM's randomness.Turned out, because I defined MathAnswer in that order, the model output was in the same order and it put the `reasoning` after the `answer`, so the thinking process didn't influence the answer like `{"answer": 67, "reasoning": "..."}` instead of `{"reasoning": "...", "answer": 69}`. I just changed the order of the model's properties and the problem was gone.
[1] https://python.useinstructor.com/#what-is-instructorETA: Codex and Claude Code only said how shit my prompt and RAG system were, then suggested how to improve them, but it only made the problem worse. They really don't know how they work.
I don't really understand the point around error handling. Sure, with structured outputs you need to be explicit about what errors you're handling and how you're handling them. But if you ask the model to return pure text, you now have a universe of possible errors that you still need to handle explicitly (you're using structured outputs, so your LLM response is presumably being consumed programmatically?), including a whole bunch of new errors that structured outputs help you avoid.
Also, meta gripe: this article felt like a total bait-and-switch in that it only became clear that it was promoting a product right at the end.
While I agree that you must be careful when using structured outputs, the article doesn't provide good arguments:
1. In the examples provided, the author compares freeform CoT + JSON output vs. non-CoT structured output. This is unfair and biases the results towards what they wanted to show. These days, you don't need to include a "reasoning" field in the schema as mentioned in the article; you can just use thinking tokens (e.g., reasoning_effort for OpenAI models). You get the best of both worlds: freeform reasoning and structured output. I tested this, and the results were very similar for both.
2. Let Me Speak Freely? had several methodological issues. I address some of them (and .txt's rebuttal) here: https://dylancastillo.co/posts/say-what-you-mean-sometimes.h...
3. There's no silver bullet. Structured outputs might improve or worsen your results depending on the use case. What you really need to do is run your evals and make a decision based on the data.
BTW, the structured outputs debate is significantly more complicated than even your own post implies.
You aren't testing structured outputs+model alone, you are testing
1. The structured outputs backend used. There are at least 3 major free ones, outlines, xgrammer, lm-format-enforcer and guidance. OpenAI, Anthropic, Google, and Grok will all have different ones. They all do things SIGNIFICANTLY differently. That's at least 8 different backends to compare.
2. The settings used for each structured output backend. Oh, you didn't know that there's often 5+ settings related to how they handle subtle stuff like whitespaces? Better learn to figure out what these settings do and how to tweak them!
3. The models underlying sampling settings, i.e. any default temperature, top_p/top_k, etc going on. Remember that the ORDER of application of samplers matters here! Huggingface transformers and vLLM have opposite defaults on if temperature happens before sampling or after!
4. The model, and don't forget about differences around quants/variants of the model!
Almost no one who does any kinds of these analysis even talk about these additional factors, including academics.
Sometimes it feels like I'm the only one in this world who actually uses this feature at the extremes of its capabilities.
Does anyone have more benchmarks or evals with data on this topic? The claimed 20% accuracy reduction is significant.
Structured output was one of the lesser known topics that AI consultants and course writers got a lot of mileage out of because it felt like magic. A lot of management people would use ChatGPT but didn’t know how to bridge the text output into a familiar API format, so using a trick to turn it into JSON felt like the missing link. Now that I think about it, I don’t recall seeing any content actually evaluating the impact of constrained output on quality though.
This blog post blurs the lines between output quality reduction and incorrect error handling, though. I’d like to see some more thorough benchmarking that doesn’t try to include obvious schema issues in the quality reduction measurements.
(repeating an earlier comment). The team behind Outlines has repeatedly provided evaluations that show constrained decoding improves the outputs:
- https://blog.dottxt.ai/performance-gsm8k.html
- https://blog.dottxt.ai/oss-v-gpt4.html
- https://blog.dottxt.ai/say-what-you-mean.html
I really like BAML but this post seems a little too much like a BAML funnel. Here are three methods that worked for me consistently since constrained sampling first came out:
1. Add a validation step (using a mini model) right at the beginning - sub-second response times; the validation will either emit True/False or emit a function call
2. Use a sequence of (1) large model without structured outputs for reasoning/parsing, chained to (2) small model for constrained sampling/structured output
3. Keep your Pydantic models/schemas as flat (not too nested and not too many enumarations) and "help" the model in the system prompt as much as you can
I have heard this argument before, but never actually seen concrete evals.
The argument goes that because we are intentionally constraining the model - I believe OAI’s method is a soft max (I think, rusty on my ML math) to get tokens sorted by probability then taking the first that aligns with the current state machine - we get less creativity.
Maybe, but a one-off vibes example is hardly proof. I still use structured output regularly.
Oh, and tool calling is almost certainly implemented atop structured output. After all, it’s forcing the model to respond with a JSON schema representing the tool arguments. I struggle to believe that this is adequate for tool calling but inadequate for general purpose use.
> but never actually seen concrete evals.
The team behind the Outlines library has produced several sets of evals and repeatedly shown the opposite: that constrained decoding improves model performance (including examples of "CoT" which the post claims isn't possible). [0,1]
There was a paper that claimed constrained decoding hurt performance, but it had some fundamental errors which they also wrote about [2].
People get weirdly superstitious when it comes to constrained decoding as though t somehow "limiting the model" when it's just a simple as applying a conditional probably distribution to the logits. I also suspect this post is largely to justify the fact that BAML parses the results (since the post is written by them).
0. https://blog.dottxt.ai/performance-gsm8k.html
1. https://blog.dottxt.ai/oss-v-gpt4.html
2. https://blog.dottxt.ai/say-what-you-mean.html
To be fair, there is "real harm" from constraining LLM outputs related to, for example, forcing lipograms or the letter "E" and a model responding with misspellings of words (deleted E) rather than words that don't actually have the letter "E" at all. This is why some authors propose special decoders to fix that diversity problem. See this paper and most of what it cites around it for examples of this: https://arxiv.org/abs/2410.01103
This is independent from a "quality" or "reasoning" problem which simply does not exist/happen when using structured generation.
Edit (to respond):
I am claiming that there is no harm to reasoning, not claiming that CoT reasoning before structured generation isn't happening.
> "reasoning" problem which simply does not exist/happen when using structured generation
The first article demonstrates exactly how to implement structured generation with CoT. Do you mean “reasoning” other than traditional CoT (like DeepSeek)? I’ll have to look for an reference but I recall the Outlines team also handling this latter case.
It seems like this could be solved by partial structured output, where the structure of the JSON itself is constrained, but the values of the JSON entries is not (so even if "quantity" here is set to int, the model can output "52.2"). Of course, we would need additional parsing, but I think it's a fair compromise.
And about structured outputs messing with chain-of-thought... Is CoT really used with normal models nowadays? I think that if you need CoT you might as well use a reasoning model, and that solves the problem.
From what I have found text -> structured text works well. I do a lot of call center based projects where I need to get intents (what API I need to call to fulfill the user’s request) and add slots (the variable part of the message like addresses).
Even Amazon’s cheapest and fastest model does that well - Nova Lite.
But even without using his framework, he did give me an obvious in hindsight method of handling image understanding.
I should have used a more advanced model to describe the image as free text and then used a cheap model to convert text to JSON.
I also had the problem that my process hallucinated that it understood the “image” contained in a Mac .DS_Store file
OpenAI structured outputs are pretty stable for me. Gemini sometimes responds with a completely different structure. Gemini 3 flash with grounding sometimes returns json inside ```json...``` causing parsing errors.
In case you're using OpenRouter, check out their new Response Healing feature that claims to solve exactly this issue.
https://openrouter.ai/announcements/response-healing-reduce-...
It does NOT. Response healing only fixes JSON syntax errors, not schema differences.
See https://news.ycombinator.com/item?id=46332119
https://github.com/josdejong/jsonrepair
might be useful ( i am not the author )
> Chain-of-thought is crippled by structured outputs
I don't know if this is true. Libraries such as Pydantic AI and I would assume the model provider SDKs stream different events. If COT is needed then a <think> section would be emitted and then later the structured response would occur when the model begins its final response.
Structured outputs can be quite reliable if used correctly. For example, I designed an AST structure that allows me to reliably generate SQL. The model has tools to inspect data-points, view their value distributions (quartiles, medians, etc). Then once I get the AST structure back I can perform semantic validation easily (just walk the tree like a compiler). Once semantic validation passes (or forces a re-prompt with the error), I can just walk the tree again to generate SQL. This helps me reliably generate SQL where I know it won't fail during execution, and have a lot of control over what data-points are used together, and ensuring valid values are used for them.
I think the trick is just generating the right schema to model your problem, and understanding the depth of an answer that might come back.
Structured outputs work well depending on the tasks. The example mentioned in the blog post output doesn’t say anything because we are missing the prompt/schema definition. Also quantity is quite ambiguous because it could be bananas as a term is readable once on the receipt.
I would love some more detailed and reproducible examples, because the claims don’t make sense for all use cases I had.
It’s not surprising that there could be a very slight quality drop off for making the model return its answer in a constrained way. You’re essentially forcing the model to express the actual answer it wants to express in a constrained language.
However I would say two things: 1. I doubt this quality drop couldn’t be mitigated by first letting the model answer in its regular language and then doing a second constrained step to convert that into structured outputs. 2. For the smaller models I have seen instances where the constrained sampling of structured outputs actually HELPS with output quality. If you can sufficiently encode information in the structure of the output it can help the model. It can effectively let you encode simple branching mechanisms to execute at sample time
> You’re essentially forcing the model to express the actual answer it wants to express in a constrained language.
You surely aren't implying that the model is sentient or has any "desire" to give an answer, right?
And how is that different from prompting in general? Isn't using english already a constraint? And isn't that what it is designed for, to work with prompts that provide limits in which to determine the output text? Like there is no "real" answer that you supress by changing your prompt.
So I don't think its a plausible explanation to say this happens because we are "making" the model return its answerr in a "constrained language" at all.
> You surely aren't implying that the model is sentient or has any "desire" to give an answer, right?
The model is a probabilistic machine that was trained to generate completions and then fine tuned to generate chat style interactions. There is an output, given the prompt and weights, that is most likely under the model. That’s what one could call the model’s “desired” answer if you want to anthropomorphize. When you constrain which tokens can be sampled at a given timestep you by definition diverge from that
A 3rd alternative is to use the best of both worlds. Have the model respond in free-form. Then use that response + structured output APIs to ask it for json. More expensive, but better overall results. (and you can cross-check between your heuristic parsing vs. the structured output, and retry / alert on miss-matches)
I am doing this with good success parsing receipts with ministral3:14b. The first prompt describes the data being sought, and asks for it to be put at the end of the response. The format tends to vary between json, bulleted lists, and name: value pairs. I was never able to find a good way to get just JSON.
The second pass is configured for structured output via guided decoding, and is asked to just put the field values from the analyzer's response into JSON fitting a specified schema.
I have processed several hundred receipts this way with very high accuracy; 99.7% of extracted fields are correct. Unfortunately it still needs human review because I can't seem to get a VLM to see the errors in the very few examples that have errors. But this setup does save a lot of time.
I would like to see a real example, the one given is assuming wanting float and assigning int.
What if you put “float” instead of int to get the required number?
Also the post is missing another use case, enums in structured data. I’ve been using it successfully for a few months now and it’s doing a fantastic job.
or tell it to output the data at the end as markdown and then do a second pass with a cheaper model to build the structured output
also, xml works much better than json, all the model guides say this
If this analysis is sound, I wonder if it can be mitigated by using tools instead of structured outputs.
Interesting read and perspective. I had very good results with structured outputs, both text, images and tool calling. Also a lot of SDKs are using it, including Vercel AI SDK.
Thanks for sharing
I don’t know if 0,42 should be the quantity
They worked fine for me. Keep working at it until results are positive instead of rabbit holing into a failure mode with a blog post.
It's usually more productive to right about how LLMs work rather than how they don't. In this case especially, there's improvements that can be made to the schema, without forfeiting on the idea of schemas altogether
Skill problem not an LLM problem
"CoT x JSON means you can't get JSON" is 2024.
Every model has built-in segmentation between reasoning/CoT + JSON.
Doesn't the Claude APIs recently introduced ability to combine extended thinking with structured outputs overcome this issue? You get the unconstrained(ish) generation in the extended thinking blocks and then structured formatting informed by that thinking in the final output.
No, structured outputs do NOT degrade output quality, at least not in the ways you claim. How many times do we have to debunk this FUD, old man?
https://blog.dottxt.ai/say-what-you-mean.html
The blog post is doubly bad because any "failures" involving images and image understanding can't necessarily be traced back to structured generation at all!!!
please i cant take anymore anti ai hot takes.
Sounds like a you problem. I’m all for people investigating the boundaries of model capability - if you take that as a personal attack, you’re going to have a bad time over the next few years.
While I suspect "hot take" is apt, this wasn't exactly anti-AI. Rather, the author is advocating for their particular way of doing genAI output parsing (as opposed to constrained decoding structured output, they advocate unconstrained decoding with a permissive parsing framework)
How is this an "anti AI hot take"? It's discussing using one type of LLM output versus another...