BAML vs POML vs YAML vs JSON for LLM Prompts

Picture this. You're debugging a prompt that worked perfectly yesterday. Now it's returning malformed JSON that crashes your parser. The model added some extra commentary. Or dropped a comma. Or wrapped everything in markdown code blocks.

Sound familiar? Most developers hit this wall within weeks of shipping their first LLM feature. The problem isn't the model. It's that we're using formats designed for configuration files to handle something much more complex.

Here's what most people miss: LLM outputs aren't just data. They're conversations that happen to contain data. When you ask a model to return JSON, you're asking it to speak in a foreign language while thinking in English. Sometimes it works. Often it doesn't.

The solution isn't better prompting. It's better formats. Four options dominate: JSON, the universal workhorse; YAML, the config file favorite; POML, an experimental schema idea; and BAML, which treats every prompt like a typed function.

Early results show BAML catching errors before runtime and shaving tokens thanks to concise type definitions. But is it worth learning a new format when YAML and JSON already work?

Why Your Prompts Keep Breaking

The dirty secret of LLM development is that most failures happen at the edges. Your prompt works in testing. It works with the examples you tried. Then it ships to production and starts failing 3% of the time.

That 3% isn't random. It happens when the model gets creative. When it adds explanations before the JSON. When it uses different field names. When it decides to format things slightly differently than your training examples.

JSON makes this worse because it's unforgiving. One missing comma and the whole thing fails. YAML is more flexible but creates different problems. A misplaced space can change the entire structure. Both formats assume the model will output exactly what you expect, exactly how you expect it.

But here's the thing about language models: they're probabilistic. They don't follow rules. They follow patterns. And patterns have variations.

Most teams solve this with increasingly complex prompts. "Return valid JSON." "Don't add any commentary." "Use exactly these field names." The prompts get longer. The examples get more detailed. The failure rate stays the same.

The real problem is deeper. JSON and YAML were designed for machines talking to machines. LLMs are something different. They're machines that think like humans but need to output like machines. The format should account for that.

What Each Format Actually Does

JSON treats LLM output like an API response. Strict structure, precise syntax, no room for interpretation. When it works, it's fast and universal. When it breaks, it breaks completely.

YAML trades brackets for indentation. More readable for humans, fewer tokens for models. But indentation errors are invisible until something breaks. And different parsers handle edge cases differently.

POML remains mostly theoretical. The idea is sound: schema-first prompting with better error handling. But without real documentation or tooling, it's hard to evaluate.

BAML takes a different approach. Instead of asking models to output data formats, it asks them to fulfill function contracts. You define what goes in and what comes out. The parser handles the rest.

Here's a BAML prompt:

class Experience {
  company: str
  years: int
}

fn extract_experience(description: str) -> Experience {
  """Extract work history from job description."""
}

That's it. No examples of exactly how to format the output. No warnings about commas and brackets. Just a clear contract: give me a company name and years of experience.

The difference becomes obvious when things go wrong. JSON fails silently or crashes. YAML fails in mysterious ways. BAML's parser knows what you wanted and tries to extract it even when the model gets chatty.

The Token Economics Problem

Every character in your prompt costs money. JSON schemas are verbose. A simple object definition might look like this:

{
  "type": "object",
  "properties": {
    "company": {"type": "string"},
    "years": {"type": "integer"}
  },
  "required": ["company", "years"]
}

That's 140 characters for two fields. The BAML equivalent is 50 characters. Scale that up to real schemas with dozens of fields and nested objects. The token savings add up fast.

YAML helps with token count by dropping the brackets and quotes. But you still need examples to show the model what good output looks like. And examples are expensive in token terms.

BAML's schemas are compact by design. Studies show 60% fewer tokens than equivalent JSON schemas, cutting costs while making the model's job clearer.

This matters more than it sounds. When you're running thousands of prompts per day, token efficiency isn't just about cost. It's about fitting more context into each request. More context means better outputs.

Developer Experience That Actually Works

Open a JSON prompt file and what do you see? Text. Maybe some syntax highlighting if you're lucky. Want to test it? Copy the prompt, paste it into a playground, run it, copy the result back, try to parse it. Rinse and repeat.

YAML is slightly better. At least the structure is readable. But testing still requires the same copy-paste dance.

BAML changes this completely. Install the VS Code extension and .baml files become interactive. You can run prompts directly in the editor. Preview the request. See the parsed output. Check for type errors. All without leaving your IDE.

The extension shows you exactly what the model receives and returns. When something breaks, you see why. When the parser fixes malformed output, you see how. The feedback loop goes from minutes to seconds.

This isn't just convenience. It's a fundamental change in how you develop with LLMs. Instead of guess-and-check cycles, you get immediate feedback. Instead of debugging in production, you catch problems during development.

YAML and JSON will always be text files that need external tools. BAML feels like programming.

The Type Safety Revolution

Most LLM failures come from mismatched expectations. You expect a number, the model returns a string. You expect an array, it returns an object. You expect specific fields, it uses different names.

Untyped formats can't help with this. You write a prompt, hope for the best, and handle errors at runtime. Every edge case becomes a special case.

BAML enforces contracts. If you define a function that returns an integer, the parser ensures you get an integer. If the model returns "approximately 5," BAML extracts 5. If it returns "five," BAML converts it. If it returns something completely different, you get a clear error.

This schema-aligned parsing is the key insight. Instead of forcing models to output perfect syntax, let them output natural language and extract the structure automatically.

Traditional approaches fight the model's natural behavior. Type-safe approaches work with it.

When Each Format Makes Sense

Use JSON when you're integrating with existing systems that expect it. When you need universal compatibility. When your prompts are simple and unlikely to break.

Use YAML when you're prototyping quickly. When token efficiency matters and you don't mind manual validation. When your team is already comfortable with YAML configs.

Use BAML when you're building production systems. When type safety matters. When you want better tooling and faster iteration cycles.

Skip POML until it has real documentation and tooling. Promising ideas don't ship products.

The pattern is clear. Simple prompts can get away with simple formats. Complex systems need structured approaches.

The Enterprise Angle

Here's where things get interesting for larger teams. BAML's type system doesn't just help with individual prompts. It enables better collaboration.

When prompts are functions with clear signatures, they become reusable components. Different teams can consume the same typed interfaces without worrying about implementation details. Backend engineers can iterate on prompts without breaking frontend code.

This matters more as LLM features become core infrastructure rather than experiments. You need the same engineering practices you use for APIs: versioning, contracts, backward compatibility.

JSON and YAML prompts are like microservices that communicate through documentation and hope. BAML prompts are like libraries with stable interfaces.

The Learning Curve Reality

BAML requires learning a new syntax. That's friction your team might not want. JSON and YAML are already burned into muscle memory.

But here's the counterintuitive part: BAML might actually be easier for new team members. The function syntax is familiar to anyone who's written code. The type system catches mistakes immediately. The tooling provides instant feedback.

Compare that to debugging YAML indentation errors or hunting down JSON parsing failures. The upfront learning cost might be lower than the ongoing debugging cost.

Of course, this depends on your team and timeline. If you need to ship something tomorrow, use what you know. If you're building something that will last, consider the total cost of ownership.

What This Means for the Future

The format you choose today shapes how your team thinks about LLM development. JSON and YAML encourage thinking of prompts as strings with structured output. BAML encourages thinking of them as functions with type contracts.

This philosophical difference matters more than syntax. Functions can be tested, versioned, and composed. Strings with examples are harder to maintain at scale.

We're still early in figuring out how to build with LLMs. The tools and practices are evolving quickly. But one pattern is clear: the teams that treat LLM integration like software engineering rather than scripting are building more reliable systems.

BAML represents that engineering approach. Whether it becomes the standard remains to be seen. But the direction it points toward, typed prompts with better tooling, feels inevitable.

Making the Decision

If you're starting a new project, try BAML. The learning curve is real but short. The productivity gains compound over time. The type safety catches problems early.

If you're working with existing systems, JSON or YAML might be simpler integration paths. But consider the long-term maintenance cost.

If you're building something experimental, YAML gives you the fastest path to working code. Just be prepared to refactor when it grows complex.

The meta-lesson is simpler: choose formats that encourage good practices. In LLM development, that means clear contracts, fast feedback loops, and graceful error handling.

The format matters less than the principles. But some formats make good principles easier to follow.

BAML's open-source approach means you can evaluate it without vendor lock-in. The VS Code extension is free. The compiler runs locally. If it doesn't work for your team, you haven't lost anything but time.

That's probably the right way to think about any new tool in this space. Try it, measure the results, keep what works. The LLM landscape changes too quickly for permanent commitments to anything except good engineering practices.