David Hughes, Principal Data & AI Solution Architect at Enterprise Knowledge (EK), was recently interviewed by Ben Lorica on Gradient Flow’s The Data Exchange podcast to discuss

The episode 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. This approach fundamentally changes prompt engineering by focusing on output schemas rather than crafting perfect prompt text, resulting in more robust applications that can adapt to new models without significant refactoring. BAML’s polyglot nature, testing capabilities, and runtime adaptability make it particularly valuable for enterprise environments and agentic AI applications, including multimodal systems.

Ahead of the episode’s release, an excerpt of their conversation was made available in Gradient Flow’s newsletter – in the article, “Faster Iteration, Lower Costs: BAML’s Impact on AI Projects,” Hughes discusses his BAML “aha moment,” how he approaches using BAML in AI solutioning, and the pros and cons of BAML when it comes to the AI project lifecycle. He also shares his excitement for what’s next, and provides a few tips and tricks for new adopters as they get started.

You can now listen to the episode here, available through Apple, Spotify, or wherever you get your podcasts.

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In this presentation, Hughes discussed an underexplored dimension of GraphRAG–the integration of image–by introducing Multimodal GraphRAG, an innovative framework that brings image data to the forefront of graph-based reasoning and retrieval. He demonstrated how this approach enables more comprehensive understanding of images, amplifying both the depth and accuracy of insights. Attendees gained insight into:

• How mmGraphRAG works;
• The integration of vision models, hypervectors, and graph databases;
• BAML agentic workflows; and
• Real-world applications and benefits for mmGraphRAG.

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