This hands-on workshop introduces a proposal that treats LLMs as computing elements governed by established software development principles—particularly task decomposition and modularization—at both the programming model (Mellea) and model level (LLM intrinsics).x000D x000D LLM outputs are often unpredictable and incorrect. Agentic frameworks and prompt optimization libraries attempt to manage this by giving control to the LLM, but this leads to systems that are hard to debug, maintain, and scale. Mellea offers an alternative: a programming model that restores developer control through modular design, information hiding, and compositional contracts. This enables predictable fault models, better portability, and lower inference costs. Attendees will gain hands-on experience building applications using the Melleaic approach.x000D x000D Extending these principles to the model level, the workshop introduces a modularization framework for LLMs using activated LoRAs. These produce components—LLM intrinsics—that match fine-tuned model accuracy for specific tasks but with significantly lower inference costs and latency, thanks to KV cache reuse. Participants will build applications using a pre-built library of RAG LLM intrinsics and learn how to train their own.x000D x000D Presented by the creators of Mellea and the inventors of LLM intrinsics and aLoRA, this workshop equips attendees with foundational skills for scalable model/application co-design.