Prompts as Functions: The BAML Revolution in AI Engineering
Apr 3, 2025
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David Hughes, a Principal Data & AI Solution Architect at Enterprise Knowledge, shares insights on BAML, a revolutionary framework that turns prompts into structured functions for AI applications. He discusses the flexibility of treating prompts as functions, which enhances developer productivity and reduces costs. The conversation also explores the capabilities of BAML in agentic AI development, advancements in multimodal retrieval, and the dynamic construction of knowledge graphs, all aimed at improving accuracy and explainability in language models.
BAML revolutionizes AI engineering by treating prompts as structured functions, enhancing determinism and maintainability in application development.
The schema-based approach of BAML enables efficient iteration and flexibility, facilitating rapid adaptation to evolving AI model landscapes.
Deep dives
Aha Moment and Introduction to BAML
The guest shares an impactful realization about BAML during a morning run while listening to a podcast interview with the CEO of Boundary ML. Prior to discovering BAML, he struggled with traditional frameworks that required extensive refactoring with any changes in input or models, leading to a brittle development process. His 'aha moment' came when he learned that BAML treats prompts as structured functions, allowing for a more rigorous and comprehensive interaction with language models. Consequently, he found that he would not need to rely on other frameworks anymore after testing BAML, marking a significant shift in his approach to prompt engineering.
Shift in Prompt Engineering with Schema-based Approach
By utilizing BAML, the guest shifted his focus from crafting elaborate prompts to defining schemas, which behave like classes. This schema-based approach allows him to concentrate on the deterministic outputs he wants from language models without the need to manipulate prompts extensively. He highlighted the practical benefit of using BAML's playground, which streamlines iteration by enabling developers to test prompts directly within their development environment. This compression of the iteration loop significantly enhances developer productivity and makes the process of prompt engineering more efficient.
BAML's Advantages Over Traditional Frameworks
BAML is presented as a replacement for traditional frameworks like LangChain and Haystack, due to its focus on generating deterministic outputs rather than merely crafting prompts. The guest emphasizes BAML's ability to enable rapid iteration and dynamic refactoring at runtime, addressing the brittleness often found in other prompt-based systems. Furthermore, he points out that developers can work with multiple language models more flexibly without becoming locked into a single provider. This adaptability is crucial in a rapidly evolving landscape of new model releases, allowing for easier transitions and testing of various models.
Multimodal GraphRAG and Automated Knowledge Graph Construction
The discussion delves into the emerging concept of multimodal GraphRAG, where different data formats, including images and audio, are integrated into knowledge graphs to enhance context and retrieval quality. The guest outlines how BAML can facilitate the automatic construction of knowledge graphs by decomposing various types of media, thus enabling efficient representation and retrieval. He also emphasizes that this innovation holds potential for superior performance in agentic systems, allowing for runtime adjustments to schemas based on real-time data needs. The integration of BAML in these processes is framed as not only future-relevant but already applicable, showcasing its adaptability in current multimodal projects.
David Hughes, Principal Data & AI Solution Architect at Enterprise Knowledge. Our discussion centers on BAML, a domain-specific language that transforms prompts into structured functions with defined inputs and outputs, enabling developers to create more deterministic and maintainable AI applications.