This page is for readers who want more than trends and surface-level opinions. The focus here is on practical thinking around LLMs, agentic AI, FastAPI, deployment workflows, and the technical tradeoffs that shape real AI products.
Building an impressive prompt is easy. Building an LLM system that stays useful under real usage is much harder. This article explores architecture choices, observability, evaluation, fallback handling, and the engineering discipline required once an AI product has to work consistently.
RAG is one of the most practical concepts in modern AI engineering. This guide explains retrieval, chunking, embeddings, vector search, and what it takes to build answers that stay grounded in real source material.
Not every problem needs an agent. In many cases, a smaller and more reliable workflow delivers better results. This piece looks at where agentic systems create real value, where they add unnecessary complexity, and how to think more clearly before adopting them.
Fine-tuning can be powerful, but it is not always the first answer. This article looks at where fine-tuning genuinely improves outcomes, how it compares with prompting and retrieval-based systems, and what teams should measure before investing in it.
