About Boundary

Provides a programming language, BAML, designed for generating and parsing structured data from large language models (LLMs) with type safety and schema enforcement. It addresses common issues like JSON parsing errors, unescaped characters, and inconsistent outputs, enabling developers to achieve reliable function-calling and reduce token usage across various programming languages.

```xml <problem> Generating structured data from large language models (LLMs) often leads to issues such as JSON parsing errors, unescaped characters, and inconsistent output formats, hindering reliable function calling and data extraction. Existing methods lack type safety and schema enforcement, increasing development overhead and runtime errors. </problem> <solution> BAML is a programming language designed to generate and parse structured data from LLMs with improved reliability and type safety. It addresses common LLM output issues by providing a parser built specifically for LLMs, which can handle errors like trailing commas, unquoted keys, and unescaped quotes. BAML enables developers to define schemas and enforce data types, ensuring consistent and predictable outputs across different LLMs and programming languages. By transforming prompt engineering into a coding process, BAML simplifies prompt management, reduces token usage, and improves the overall efficiency of LLM-based applications. </solution> <features> - Expressive syntax for defining structured data schemas and prompts - Parser designed to handle common LLM output errors, including JSON parsing issues - Type-safe data generation and parsing across Python and TypeScript - Static analysis capabilities for identifying potential issues before runtime - Support for classifiers, multimodal inputs, and dynamic prompts - Playground environment for testing and iterating on BAML code - Integration with various LLMs, improving function-calling performance </features> <target_audience> BAML is targeted towards developers and data scientists building applications that rely on structured data extraction from LLMs, particularly those working on function calling, data validation, and prompt engineering. </target_audience> ```

What does Boundary do?

Where is Boundary located?

Boundary is based in Seattle, United States.

When was Boundary founded?

Boundary was founded in 2023.

Location

Seattle, United States

Founded

2023

Employees

6 employees

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Boundary

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Executive Summary

boundaryml.com 700+

Founded 2023 – Seattle, United States

Funding

No funding information available.

Team (5+)

No team information available.

Company Description

Problem

Generating structured data from large language models (LLMs) often leads to issues such as JSON parsing errors, unescaped characters, and inconsistent output formats, hindering reliable function calling and data extraction. Existing methods lack type safety and schema enforcement, increasing development overhead and runtime errors.

Solution

BAML is a programming language designed to generate and parse structured data from LLMs with improved reliability and type safety. It addresses common LLM output issues by providing a parser built specifically for LLMs, which can handle errors like trailing commas, unquoted keys, and unescaped quotes. BAML enables developers to define schemas and enforce data types, ensuring consistent and predictable outputs across different LLMs and programming languages. By transforming prompt engineering into a coding process, BAML simplifies prompt management, reduces token usage, and improves the overall efficiency of LLM-based applications.

Features

Expressive syntax for defining structured data schemas and prompts

Parser designed to handle common LLM output errors, including JSON parsing issues

Type-safe data generation and parsing across Python and TypeScript

Static analysis capabilities for identifying potential issues before runtime

Support for classifiers, multimodal inputs, and dynamic prompts

Playground environment for testing and iterating on BAML code

Integration with various LLMs, improving function-calling performance

Target Audience

BAML is targeted towards developers and data scientists building applications that rely on structured data extraction from LLMs, particularly those working on function calling, data validation, and prompt engineering.