Architecture notes, engineering delivery, applied AI systems, and cloud strategies that scale.
The AI industry has spent the last few years making language models better at generating text.
When an organization starts using AWS, the first few teams can usually create resources manually:
Many AI teams monitor latency and token usage but still don't know why their production AI system is failing.
A model that ranks #1 on a public benchmark may be the wrong model for your production workload.
Vector search is powerful because it retrieves documents based on semantic meaning, not just exact words.
A production RAG system is not one component. It is a multi-stage retrieval and generation pipeline, and every stage can introduce failure.
production systems need to handle everything that happens around that LLM
When building an AI application, it's tempting to tightly integrate everything with one LLM provider.It works initially.But as the application grows, that decision can be...
In a growing engineering team, multiple developers, DevOps engineers, platform engineers, and automation pipelines are continuously provisioning AWS resources.