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.