LLM
LLM System Thinking: Product, Engineering, and Cost Must All Work Together
Many AI projects focus on isolated model quality and ignore the system around it. A better answer does not automatically mean a better produ
LLM
Many AI projects focus on isolated model quality and ignore the system around it. A better answer does not automatically mean a better produ
Python
AI tooling becomes valuable when it enters everyday developer workflows, not when it only shines in a demo window. Python is an ideal layer
Java
Too much engineering governance depends on a few experienced people remembering the right risks at the right time. Platform thinking changes
LLM
Many language model projects slow down after launch because they never build a real evaluation loop. A system that looks impressive in a sma
Python
Scripts are often dismissed as disposable helpers, but they can become a lightweight observability layer when they share logs, context, and
Java
Event-driven systems are often introduced as a clean way to decouple services. The harder challenge comes later: how to keep meaning, sequen
LLM
The hard part of agents is not tool calling. It is recovery. Models plan, tools execute, and the system must absorb mistakes without collaps
Python
Data issues are difficult because they usually begin at the front of the pipeline and surface at the end. Poor model outputs, unstable repor
Java
Task scheduling often starts as a handful of cron jobs and slowly becomes a platform responsibility. At that point, scaling is no longer abo
LLM
Many RAG projects appear complete because retrieval, prompting, and generation are wired together. Yet the answers still drift. The weakness
Python
Most automation scripts solve a problem once and then quietly decay. Durable Python automation is different: it has parameters, logging, err
Java
A resilient Java service is not one that never fails. It is one where a local failure cannot spread across the whole system. Thread-pool iso