- Developers: Building applications that rely on structured data output from LLMs.
- Machine Learning Engineers: Integrating LLMs into workflows where reliable data formats are crucial.
- Data Scientists: Extracting specific, structured information from large volumes of text generated by LLMs.
- AI Researchers: Experimenting with and optimizing LLM interactions for structured output.
- Anyone working with LLMs: Who struggles with inconsistent or verbose LLM responses and needs clean, formatted data.
- Software Architects: Designing robust systems that leverage LLMs for data processing and automation.
How to Use BoundaryML.com?
- Installation: Begin by installing BAML into your Python environment using pip: `pip install baml-py`.
- Define Schema & Functions: Use BAML's expressive syntax to define the desired output schema (e.g., JSON structure) and functions for your LLM interactions. This transforms prompt engineering into a more structured coding approach.
- Integrate with LLMs: Connect BAML with your chosen LLM (e.g., GPT-3.5, GPT-4o). BAML's parser and function-calling capabilities work across various models.
- Develop in Playground: Utilize the BAML playground environment to develop, test, and refine your structured text generation workflows.
- Get Structured Output: Execute your BAML code. The parser ensures robust JSON error correction, handles "LLM yapping," and coerces output to your defined schema, providing reliable structured data.
- LLM-Specific Parser: Features a robust parser built from the ground up for LLMs, offering superior JSON error correction, immunity to irrelevant LLM chatter ("LLM yapping"), and automatic schema coercion.
- Universal Function-Calling: Enables reliable function-calling across virtually every LLM, providing consistent performance regardless of the underlying model.
- State-of-the-Art Performance: Achieves benchmark-setting results in function-calling, particularly noted for its efficiency and token reduction with models like GPT 3.5.
- Prompt Engineering as Code: Transforms the often-unpredictable art of prompt engineering into a structured, type-safe coding process with features like classifiers, multimodal inputs, and dynamic prompts.
- Static Analysis & Type-Safety: Provides static analysis and type-safety for LLM interactions, reducing errors and increasing reliability in development.
- Simplifies the complex task of getting structured data from LLMs.
- Robust parser with excellent JSON error correction and "anti-yapping" capabilities.
- Enables reliable function-calling across a wide range of LLM models.
- Transforms prompt engineering into a more reliable and systematic coding process.
- Achieves state-of-the-art results in LLM function-calling benchmarks.
- Offers features like static analysis and type-safety for increased development reliability.
- Supports multi-modal inputs, expanding LLM interaction possibilities.
- Includes a dedicated playground for easier development and testing.
- Helps in reducing token usage and improving efficiency for LLM interactions.
- Primarily a developer tool, not suitable for non-technical users seeking a no-code solution.
- Requires installation and familiarity with coding environments (e.g., Python).
- Learning curve associated with adopting a new language syntax (BAML).
- Currently focused on structured text generation; might not cover all LLM interaction needs.
- Specific performance gains or limitations for lesser-known LLMs are not explicitly detailed.
Starter
$ 0.00
VSCode playground testing, with multimodal capabilities
VSCode playground real-time prompt preview
Community support via Discord and Github
Enterprise
custom
Dedicated Slack channel
Architectural reviews with our experienced AI engineers
Prioritized feature requests
Access to Boundary Studio
Boundary Studio includes observability, data labeling, fine-tuning engineering support and more
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Editorial Note
This page was researched and written by the ATB Editorial Team. Our team researches each AI tool by reviewing its official website, testing features, exploring real use cases, and considering user feedback. Every page is fact-checked and regularly updated to ensure the information stays accurate, neutral, and useful for our readers.
If you have any suggestions or questions, email us at hello@aitoolbook.ai