Skip to content

Book a call

Eugene, Oregon · one person, whole builds

Tagged

Rag

4 posts on rag.

Anatomy of an agent harness: hooks, loops, memory, RAG

The model is the one part of an agentic system I did not build and cannot pin down. Everything around it is scaffolding I can test: hooks that block, a loop that verifies, files that remember, a ladder that routes, retrieval that has to stay true. Six components, what each one decides, and the failure that put it there.

Graph RAG vs vector RAG: when the graph earns its keep

Vector retrieval answers the question of what resembles this text, while a graph answers what is connected to it, and the two fail on different questions. The benchmarks say the difference is large on relational questions and close to nothing on lookups, which makes this an accounting decision rather than an architecture debate. What each one costs, the hybrid I actually run, and how to decide before you buy an index.

RAG that stays current: knowledge that ships with the feature

Most retrieval systems do not fail at retrieval: they fail because the paragraph they found describes software that no longer exists. Keeping RAG up to date is a scheduling problem before it is an engineering one, so I made it a rule: an admin feature is not done until the migration that ships it has also written the chunks that describe it, and the embed step has run.

The questions similarity search can't answer

The assistant on this site can find anything that resembles your question. It cannot yet follow a chain of facts spread across three notes. Why that failure is structural, what a knowledge graph actually is, and how I plan to wire one into the studio's retrieval without tearing out what works.

← All insights