Vector Search Isn't Always Enough for RAG
Vector search is powerful because it retrieves documents based on semantic meaning, not just exact words.
Cloud & AI Solutions Architect
Notes on architecture, delivery, AI systems, and software that lasts.
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.
As AWS environments grow, one of the first operational challenges organizations face is resource management.
Type "gift for my wife" into a traditional e-commerce search box built on BM25/TF-IDF keyword matching, and you'll get exactly what you asked for: nothing useful
You make the decision to upgrade your EBS volume through the AWS Console, eagerly wait for the "volume modification" to complete, and then SSH into your instance... only to run df -h and see the same old storage size staring back at you.
Stop serving AI crawlers for free – now you can charge them at the edge, with no code changes.