Applied AI

Hands-on notes from building AI infrastructure: RAG pipelines, vector databases, taking LLM-backed services to production, and the formats and tooling that agents rely on. Each post is code-first and covers what worked and what didn’t.

Read oldest to newest, the posts follow a progression: RAG fundamentals, then vector store tradeoffs, then a production Q&A service, then knowledge formats for agents.

Pinecone Goes Serverless: What Actually Changes

4 minute read

Pinecone announced their serverless architecture in early 2024, and after using both the pod-based and serverless versions on a few small projects, I have so...

Building Blocks of a RAG Pipeline

5 minute read

Over the past year I’ve watched “RAG” go from a niche research acronym to something that comes up in every engineering discussion involving LLMs. If you’ve b...