Announcement
BAML: The Engineering Framework for Prompt Engineering
BAML (Boundary AI Markup Language) is an open-source framework that brings structure, testability, and version control to prompt engineering, enabling seamless integration of AI models into production applications across multiple programming languages.
Project Introduction
Summary
BAML is an AI framework designed to add engineering discipline to prompt engineering. It allows developers to define AI interactions with structured inputs/outputs, test them rigorously, and integrate them reliably into diverse programming languages, transforming prompt engineering from an art to an engineering practice.
Problem Solved
Developing reliable AI features often involves managing complex, untestable prompt strings and unstructured outputs, making versioning and integration into production codebases challenging. BAML solves this by providing a framework to define, test, and integrate AI interactions as structured components.
Core Features
Structured AI Calls (BAML Language)
Define AI calls and expected outputs using a structured, language-agnostic markup language, ensuring consistency and maintainability.
Multi-Language Compatibility
Integrate seamlessly into Python, TypeScript, Ruby, Java, C#, Rust, and Go codebases, treating AI calls like standard function calls.
Testing & Versioning
Write unit tests for your AI prompts and outputs directly within your development workflow, ensuring reliability as prompts and models evolve.
Tech Stack
Use Cases
BAML is ideal for any scenario where structured, testable, and language-agnostic integration of AI models into software applications is required.
Building Production-Ready AI Features
Details
Build features like natural language processing (NLP) components, content generation, data extraction, or classification within large-scale applications by treating AI calls as testable code modules.
User Value
Significantly reduces the complexity and risk of integrating AI, making features reliable and easier to maintain over time.
Ensuring Structured & Reliable AI Outputs
Details
Ensure that AI model outputs conform to expected data structures (e.g., JSON, specific objects) required by downstream application logic, regardless of the underlying model or programming language.
User Value
Eliminates brittle parsing logic and runtime errors caused by unexpected AI output formats.
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