BAML: Bringing Software Engineering Rigor to LLM Development
Prashanth Rao, AI Engineer working with graphs at Kùzu, Inc, explores BAML, a new domain-specific language that promises to streamline LLM integration with better developer experience.
Link, Description & Synopsis
Link:
Why I’m excited about BAML and the future of agentic workflows
Description:
Prashanth Rao, AI Engineer working with graphs at Kùzu, Inc, explores BAML, a new domain-specific language that promises to streamline LLM integration with better developer experience
Synopsis:
This articles explores how BAML:
Improves structured output generation from LLMs
Provides type-safe domain-specific language for prompts
Enables fast parsing and error correction in Rust
Supports multiple programming languages through code generation
Context
As 2025 shapes up to be the year of agents and AI workflow orchestration, developers face challenges with existing frameworks being too rigid or verbose.
BAML emerges as a solution that focuses on developer experience and reliability.
BAML massively improves the prompt engineering experience by providing a type-safe domain-specific language (DSL), and generates compact prompts that are easy to write, read and test.
…
the BAML parser obtains the LLM’s output and applies post facto fixes and validation to the output. Instead of relying on costly methods like re-prompting the LLM to fix minor issues in the outputs (which takes seconds), the BAML engine corrects the output in milliseconds, thus saving money on API calls while also allowing you to use smaller, cheaper models that can achieve largely the same outcome as bigger, more expensive models4.
BAML focuses on rapid delivery:
Quick, testable proofs-of-concept
Immediate value to stakeholders
Iterative complexity addition
End-to-end testing from day one
BALM is particularly reelvant for organizations who are developing agentic workflows and observability.
Key Implementation Patterns
The article demonstrates three key patterns:
Schema-Based Development
Type-safe DSL for prompts
Schema-Aligned Parsing (SAP)
Automatic error correction
Fast Rust-based parsing (<10ms)
A simple resume information extraction test that outputs structured data:
test my_resume {
functions [ExtractResume]
args {
resume #"
Prashanth Rao
Experience:
- AI Engineer at Kùzu Inc. (2024 Jan - Present)
"#
}
}
This simple test immediately shows results in the editor
Developer-Centric Architecture
Clean project structure
Transparent prompt management
Immediate testing capabilities
Language-agnostic approach
Token Optimization
Lossless compression in prompts (e.g., reducing 370 tokens to 168 tokens)
Reduced token usage
Cheaper API costs
More efficient LLM usage
These patterns suggest important strategic implications for teams building LLM-based systems.
Strategic Implications
For technical leaders, this suggests several key implications:
Development Efficiency
Faster iteration cycles
Reduced API costs
Better error handling
Cross-language support
Quality Assurance
Built-in testing framework
Type safety guarantees
Error correction capabilities
Transparent prompt management
Resource Optimization
Reduced token usage
Lower API costs
Faster processing
Language flexibility
To translate these implications into practice, teams need a clear implementation framework.
Implementation Framework
For teams adopting BAML, the framework involves:
Project Setup
Initialize BAML directory structure
Configure language generators
Set up client configurations
Define data models
Integration Layer
Implement type definitions
Create prompt templates
Configure error handling
Set up testing framework
System Management
Monitor token usage
Track parsing performance
Manage model configurations
Handle version updates
This implementation framework leads to several key development considerations.
Development Strategy
Key development considerations include:
Language Integration
Choose target languages
Set up code generation
Implement client libraries
Maintain language consistency
Prompt Engineering
Design type-safe prompts
Implement test cases
Optimize token usage
Monitor parsing performance
Quality Control
Validate output schemas
Test error handling
Monitor performance metrics
Track cost optimization
These considerations highlight the broader significance of BAML in the LLM development landscape.
Personal Notes
BAML represents a significant shift in LLM development by bringing software engineering best practices to prompt engineering and LLM orchestration.
Like TypeScript brought type safety to JavaScript, BAML brings structure and reliability to LLM development.
Looking Forward: LLM Development Tools
The tooling ecosystem will likely evolve to include:
More sophisticated type systems
Enhanced testing frameworks
Cross-framework compatibility
Advanced debugging capabilities
Broader language support
Conclusion
This evolution could fundamentally change how teams approach LLM development, making it more reliable, maintainable, and cost-effective.
That’s all for today :) For more AI Agents, AI Engineering, & LLM Systems treats, check out our archives.
All the best,
Sebastian Gutierrez
https://x.com/seb_g
https://sebgnotes.com