BAML: Next-Level Prompt Engineering for AI Builders 🤯

BuildAIers Toolkit #1: Modular Prompting Made Simple—BAML Transforms How AI Builders Work with LLMs. 🛠️

This review originally appeared in the MLOps Community Newsletter as part of our weekly column contributions to the community (published every Tuesday and Thursday).

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Welcome to this week’s Tuesday Tool Review (our first issue!)—where we practically review the tools shaping the AI ecosystem and help you decide what’s worth your time.

This week, we’re diving into BAML (Boundary AI Markup Language)—a domain-specific language that makes creating AI apps easier by treating LLM prompts as functions.

If you’ve been juggling messy prompt engineering workflows or struggling to integrate LLMs into multi-language projects, BAML might be the tool you’ve been waiting for! Turning prompts into modular, reusable functions makes prompt engineering less of a hassle—and a lot more scalable.

👉 See discussion in the MLOps community.

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🔧 Tool Overview: BAML in a Nutshell

BAML lets developers define prompts with explicit input parameters and expected outputs. It makes prompt engineering feel more like writing reusable code modules than creating one-time strings.

The BAML approach brings structure, consistency, and modularity to how you develop AI apps.

💡 Who’s it for?

• MLOps engineers, data scientists, and AI engineers.

• Teams integrating LLMs into diverse programming environments (Python, TypeScript, Ruby, etc.).

• Anyone tired of messy, unscalable prompt engineering workflows.

Key Benefits:

✅ Write prompts like reusable functions
✅ Easily integrate LLMs across languages
✅ Reduce prompt sprawl and boost maintainability

➡️ Check it out on GitHub

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⚙️ Setup & Usability: Built for Developers

BAML’s setup is straightforward.

# Install BAML using pip

pip install baml

# Initialize a new BAML project

baml init my-first-baml-project

• Cross-language support: Write AI-specific logic in BAML while keeping the rest of your codebase intact—whether it’s in Python, TypeScript, Ruby, or other languages.

• Use the BAML Visual Studio Code (VSCode) extension for:

  • Real-time prompt previews

  • Schema validation

  • Interactive prompt execution

• Minimal setup: BAML is super easy to set up, with great docs and a helpful community.

➡️ Get started with BAML using the Python Integration

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💡 Key Features & Strengths

✅ Language-agnostic integration: You can use LLMs without refactoring your existing codebase because you can call BAML functions from various languages.

✅ Structured prompt definition: Define prompts with clear inputs and expected outputs, turning them into predictable, testable components.

✅ Robust tooling support: The VSCode extension has awesome tools to help with writing and testing prompts, with schema-aware editing and real-time execution.

✅ Active community & open source: BAML is open-source (Apache 2.0) with an active community. Get support, contribute, or explore on GitHub and Discord.

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⚠️ Limitations & Weaknesses

😞 Learning curve for new users: You may face an initial adjustment period if you're new to domain-specific languages.

⚡ Evolving ecosystem: Obviously, BAML is pretty new and still growing, so there will be frequent updates. These developments might lead to some breaking changes every now and then, so make sure to stay on top of version updates.

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🚀 Real-World Use Case: Building a Tone-Sensitive Chatbot

Let’s say you want to build a chatbot that adjusts its tone based on user input. Using BAML, you can define a function that takes user messages and a desired tone as inputs and returns an appropriate response.

Example Python Integration (from GitHub):

from baml_client import b

from baml_client.types import Message, StopTool

messages = [Message(role="assistant", content="How can I help?")]

while True:

print(messages[-1].content)

user_reply = input()

messages.append(Message(role="user", content=user_reply))

tool = b.ChatAgent(messages, "happy")

if isinstance(tool, StopTool):

print("Goodbye!")

break

else:

messages.append(Message(role="assistant", content=tool.reply))

👆 This clean integration keeps your AI logic modular, testable, and maintainable while making it easy to tweak prompts and outputs.

➡️ Explore more examples in the docs

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🔄 Comparisons: BAML vs. Traditional Prompt Engineering

Traditional Approach:

• Embedding prompts directly into code (often as long strings)

• No clear structure for inputs/outputs

• Messy and hard to maintain

BAML Approach:

• Prompts as structured functions with defined schemas

• Reusable across multiple projects and languages

• Clear separation between AI logic and app code

Verdict: If you’ve struggled with prompt sprawl in large codebases, BAML gives a cleaner, more scalable solution.

Comparison with other frameworks:

• BAML vs Pydantic

• BAML vs Marvin

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🧑‍⚖️ Final Verdict: Is BAML Worth It?

So I’d say BAML is a pretty cool tool that makes it way easier to work with LLMs by letting you create reusable functions for your prompts.

It's super helpful for teams building complex AI systems or scaling LLM-powered apps.

There's a bit of a learning curve, but the benefits, like maintainability, modularity, and productivity, make it valuable for any LLMOps toolkit.

⭐ Rating: ⭐⭐⭐⭐☆ (4/5)

✅ Pros:

• Structured and maintainable AI code integration

• Good cross-language support

• Excellent developer experience (VSCode extension)

❌ Cons:

• Initial learning curve

• Rapid ecosystem changes may require frequent updates

🔗 Explore BAML from the docs

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💬 Have You Tried BAML?

We’d love to hear from you!

• Have you integrated BAML into your workflows?

• Got any tips, tricks, or challenges?

👇 Share your thoughts in the comments! 👇