Deep dives into Agentic Workflows, distributed systems, and the architectural rigor required to move AI from experimentation to enterprise-grade production.
In modern software organizations, AI-assisted coding is no longer a sprint of curiosity — it is a production capability that must thread through governance, observability, and delivery pipelines.
In modern AI production, the data strategy defines the model's reliability as much as the algorithm. Golden datasets anchored to expert ground truth give high-precision evaluation on defined tasks, but they rarely cover the full spectrum of real-world scenarios.
In production AI, the line between external search visibility and model-driven answer quality matters for business outcomes.
In production environments, the choice between GPT-style hosted APIs and open-weight self-hosted models is not theoretical—it maps directly to governance, risk, and delivery velocity.
In production AI, choosing the right foundation model is not simply about peak accuracy. It is a decision that reverberates through data pipelines, governance, monitoring, and the ability to deliver reliable business outcomes.
In production-grade AI, choosing between GPT-4o Vision and Gemini Vision is not about chasing the latest benchmarks alone.
GPU capacity planning for production AI is not a theoretical exercise; it is a core driver of delivery velocity and reliability.
GPU inference unlocks generation workloads at scale. For production-grade AI systems that rely on large language models, real-time responses, and complex retrieval pipelines, GPUs offer the parallelism, memory bandwidth, and batching capabilities that drive throughput.
GraphRAG and Vector RAG are two distinct patterns for building retrieval augmented AI systems in production. GraphRAG centers on a knowledge graph of entities and relations, enabling explicit reasoning over interconnected facts.