Guest Author

Vineet Vijay

Guest Author

Vineet Vijay is a Lead AI and machine learning engineer specialising in the design and deployment of intelligent systems across regulated industries. His work spans financial services, healthcare, and other high-stakes operational environments, with a focus on large language models, domain adaptation, and production-grade AI architecture. He holds a Master's degree in Data Science and Artificial Intelligence from the University of Liverpool and contributes to industry discussions on responsible AI deployment, governance standards, and the engineering foundations of reliable AI at scale.

RAG

Cutting RAG inference costs 6x starts with deciding what never reaches the LLM

Most teams building retrieval augmented generation (RAG) systems for high stakes classification make the same architectural bet: Route every ambiguous case straight to the language model and trust the retrieved context to sort it out. This works fine in a demo. It falls apart the moment the system has to survive an audit, a regulator, or a compliance officer asking why a specific decision was made six months ago.