What happens when your impressive RAG demo meets the chaos of real-world data? Dive deep into the hidden complexities of production RAG systems—where PDFs are rotated, embeddings leak secrets, and milliseconds matter. A practical guide through the architectural decisions that separate demos from deployments.

Why deploy massive LLMs when smaller, specialized models can work together more efficiently? This post explores the rise of Small Language Models (SLMs) and how composable agents, modular, cooperative, and resilient, are reshaping the future of AI architectures.

Bigger isn’t always better. From pruning and quantization to knowledge distillation and hardware-aware design, this blog examines practical techniques to make AI models faster, leaner, and more efficient, whether in the cloud, on the edge, or inside embedded systems.

Static AI can’t keep pace with a changing world. Learn how adaptive agents sense, learn, and evolve in real-time updating knowledge, refining policies, and responding to drift, while staying secure, explainable, and production ready.