A PM checks a task card in Notion → the system syncs it to a GitHub issue → writes a plan → writes code → opens a PR for human review. This post explains what the system does, what it doesn't do, and why it's feasible now — written for people who don't write code.
A six-layer deterministic pipeline that handles everything from URL ingestion to vector embedding automatically, filtering out garbage before it enters your RAG system through an eight-dimension scoring system.
RAG doesn't have to be a rigid three-step process. It's a set of steps that can be dynamically enabled, skipped, or reordered. Pipeline as Code lets the system adapt its behavior without redeployment.
"Adding a Cross-Encoder feels better" is not a scientific evaluation. A/B testing tells you whether a change actually works, how much it helps, and which query types benefit.
The hardest part of a RAG system isn't building it — it's figuring out why a particular answer went wrong. Pipeline Tracing records every step's decisions and data so debugging has a clear trail to follow.
A dynamically composable RAG pipeline built on Cloudflare Workers AI (gemma-3-12b-it + bge-m3): 14 base steps + 6 LangGraph-specific nodes, with three strategy graphs (Baseline / Agentic / Plan-Execute) selected at runtime.