For my use case (https://www.grokvocab.com/), I get proper JSON output without much effort. I am using Langchain4J (https://github.com/langchain4j/langchain4j) which automatically maps the output JSON to my POJO. I just prompt the model to return the output as JSON.

This is a seriously beautiful guide. I really appreciate you putting this together! I especially love the tab-through animations on the various pages, and this is one of the best explanations that I've seen. I generally feel I understand grammar-constrained generation pretty well (I've merged a handful of contributions to the llama.cpp grammar implementation), and yet I still learned some insights from your illustrations -- thank you!

I'm also really glad that you're helping more people understand this feature, how it works, and how to use it effectively. I strongly believe that structured outputs are one of the most underrated features in LLM engines, and people should be using this feature more.

Constrained non-determinism means that we can reliably use LLMs as part of a larger pipeline or process (such as an agent with tool-calling) and we won't have failures due to syntax errors or erroneous "Sure! Here's your output formatted as JSON with no other text or preamble" messages thrown in.

Your LLM output might not be correct. But grammars ensure that your LLM output is at least _syntactically_ correct. It's not everything, but it's not nothing.

And especially if we want to get away from cloud deployments and run effective local models, grammars are an incredibly valuable piece of this. For practical examples, I often think of Jart's example in her simple LLM-based spam-filter running on a Raspberry Pi [0]:

> llamafile -m TinyLlama-1.1B-Chat-v1.0.f16.gguf \ > --grammar 'root ::= "yes" | "no"' --temp 0 -c 0 \ > --no-display-prompt --log-disable -p "<|user|> > Can you say for certain that the following email is spam? ...

Even though it's a super-tiny piece of hardware, by including a grammar that constrains the output to only ever be "yes" or "no" (it's impossible for the system to produce a different result), then she can use a super-small model on super-limited hardware, and it is still useful. It might not correctly identify spam, but it's never going to break for syntactic reasons, which gives a great boost to the usefulness of small, local models.

* [0]: https://justine.lol/matmul/

This is a fantastic guide! I did a lot of work on structured generation for my PhD. Here are a few other pointers for people who might be interested:

Some libraries:

- Outlines, a nice library for structured generation

  - https://github.com/dottxt-ai/outlines

- Guidance (already covered by FlyingLawnmower in this thread), another nice library

  - https://github.com/guidance-ai/guidance

- XGrammar, a less-featureful but really well optimized constrained generation library

  - https://github.com/mlc-ai/xgrammar

  - This one has a lot of cool technical aspects that make it an interesting project

Some papers:

- Efficient Guided Generation for Large Language Models

  - By the outlines authors, probably the first real LLM constrained generation paper

  - https://arxiv.org/abs/2307.09702

- Automata-based constraints for language model decoding

  - A much more technical paper about constrained generation and implementation

  - https://arxiv.org/abs/2407.08103

- Pitfalls, Subtleties, and Techniques in Automata-Based Subword-Level Constrained Generation

  - A bit of self-promotion. We show where constrained generation can go wrong and discuss some techniques for the practitioner

  - https://openreview.net/pdf?id=DFybOGeGDS

Some blog posts:

- Fast, High-Fidelity LLM Decoding with Regex Constraints

  - Discusses adhering to the canonical tokenization (i.e., not just the constraint, but also what would be produced by the tokenizer)

  - https://vivien000.github.io/blog/journal/llm-decoding-with-regex-constraints.html

- Coalescence: making LLM inference 5x faster

  - Also from the outlines team

  - This is about skipping inference during constrained generation if you know there is only one valid token (common in the canonical tokenization setting)

  - https://blog.dottxt.ai/coalescence.html

BAML (https://boundaryml.com/) is really great for handling structured outputs. It's what we use at my company.

Very nicely written guide!

If the authors or readers are interested in some of the more technical details of how we optimized guidance & llguidance, we wrote up a little paper about it here: https://guidance-ai.github.io/llguidance/llg-go-brrr

Question for the well-informed people reading this thread: do SoTA models like Opus, Gemini and friends actually need output schema enforcement still, or has all the the RLVR training they do on generating code and json etc. made schema errors vanishingly unlikely? Because as a user of those models, they almost never make syntax mistakes in generating json and code; perhaps they still do output schema enforcement for "internal" things like tool call schemas though? I would just be surprised if it was actually catching that many errors. Maybe once in a while; LLMs are probabilistic after all.

(I get why you need structured generation for smaller LLMs, that makes sense.)

This is good. It covers the two easiest dominant methods people use. It even touches on my main complaint for the one they seem to recommend.

That said:

- Constrained generation yields a different distribution from what a raw LLM would provide. This can be pathologically bad. My go-to example is LLMs having a preference for including ellipses in long, structured objects. Constrained generation forces closing quotes or whatever it takes to recover from that error according to a schema, nevertheless yielding an invalid result. Resampling tends to repeat till the LLM fully generates the data in question, always yielding a valid result which also adheres to the schema. It can get much worse than that.

- The unconstrained "method" has a few possible implementations. Increasing context length by complaining about schema errors is almost always worse from an end quality perspective than just retrying till the schema passes. Effective context windows are precious, and current models bias heavily toward earlier data which has been fed into them. In a low-error regime you might get away with a "try it again" response in a single chat, but in a high-error regime you'll get better results at a lower cost by literally re-sending the same prompt till the model doesn't cause errors.

Curious what tech stack is behind this docs/cookbook page. Doesn't look like standard MkDocs/GitBook, but maybe I'm wrong.

Would love to know.

Incredible guide, wow. Will definitely share with people. I wish I had something like this a year ago.

This is a nice guide. I especially like the masked decoding diagrams on this page https://nanonets.com/cookbooks/structured-llm-outputs/basic-....

edit: Somehow that link doesn't work... It's the diagram on the "constrained method" page

I've built pipelines with lab provided structured outputs and without, one thing to be aware of is enforcing structured outputs has a performance penalty.

That might not matter to you, but it can be 2-3x slower sometimes.

Are there output formats that are more reliable (better adherence to the schema, easier to get parse-able output) or cheaper (fewer tokens) than JSON? YAML has its own problems and TOML isn't widely adopted, but they both seem like they would be easier to generate.

What have folks tried?

I agree that building agents is basically impossible if you cannot trust the model to output valid json every time. This seems like a decent collection of the current techniques we have to force deterministic structure for production systems.

> We use a lenient parser like ast.literal_eval instead of the standard json.loads(). It will handle outputs that deviate from strict JSON format. (single quotes, trailing commas, etc.)

A nitpick: that's probably a good idea and I've used it before, but that's not really a lenient json parser, it's a Python literal parser and they happen to be close enough that it's useful.

These are cool tricks but this seems like an impedence mismatch: why would you use an LLM (a probabilistic source of plausible text) in a situation where you want a deterministic source of text where plausibility is not enough?

This information is really presented well. I subscribed to your newsletter. Thanks!

I like structured outputs as much as the next guy but be careful not to try to structure natural language.

What would be the point of outputting unconstrained json if the output is consumed by a human?

The first thing I've seen is that the article uses https://xkcd.com/2347/ without a reference.. Is it famous enough to be sure everybody knows the origin?

Huge fan of BAML , nice coverage

Stupid question but isn't this useless for 99% of users? By that I mean that either your API provider supports Structured Outputs (OpenAI and Google) or it doesn't and you're SOL.

Sure the guide presents some alternatives but they're incomparably useless VS real enforced structured output.

I get that some people will run their own models or whatever and will be able to use some of the other techniques, but that's the remaining 1%.

BAML