Building Reliable AI Over Company Knowledge
Build an assistant that answers from company documents, one gap at a time. A core everyone reads, two parts on measuring and watching it, then modules you add only when your users' questions need them.
before you start
- Comfortable reading Python
- Python 3.11 or later to run the companion code; nothing else to install, and no API key
by the end, you can
- Build a retrieval-augmented assistant over company documents and explain what each stage does
- Turn scans, split tables, slides, and figures into evidence a model can use
The series builds one assistant for Atlas Bikes, a fictional e-bike company, over the kind of documents most companies have: warranty policies in Word, specifications as PDFs, a scanned assembly guide, training slides, and a web page of error codes. Each part starts from a question the previous version gets wrong and ends with a version that gets it right. Every part ends with a system that is complete for the questions it answers, and says when you can stop there. Few systems need all fourteen parts; Part 14 helps you choose.
Every part comes with a tagged version of a runnable Python project. Moving from one tag to the next shows exactly what a part changed, and every output in the articles comes from running that version. The parts that would call a model use deterministic stand-ins, so the code runs offline, without an API key, and gives the same result every time. A version with real models, on the same tags, is planned, followed by three companion parts on what production adds: access and trust, keeping the knowledge current, and speed and cost.