Graph RAG Is Not Better RAG Picture a small experiment. You have a handful of short documents about drug interactions - except the drugs are invented. Not real names: Faxiprine, Tricalamine, Renal Shimmer, Hydrolyx Draught. That matters, because if the drugs were real, a language model could answer from what it already learned in training and we’d never know whether retrieval was doing any work. Fictional drugs force every answer to come from the documents in front of it. Nowhere else. ...

Why Raw Queries Break RAG Here’s the pitch for RAG in one sentence: embed the user’s question, search a vector store for the closest matching chunks, hand those chunks to an LLM, get a grounded answer back. Clean, mechanical, almost too simple to fail. So why do RAG systems keep retrieving the wrong thing? The pitch skips a step it shouldn’t. Before you can ask whether retrieval “worked,” you have to ask what the system was actually comparing — and once you look closely at that comparison, a raw, naturally-typed question turns out to be a strange thing to search with. ...

Procedural Memory Is a Pattern, Not a Fact This post assumes you’re already familiar with procedural memory in the agent memory management paradigm. If that’s not the case, the first post will give you enough context before you continue. There are three places an agent can be wrong about a fact it has about you: extraction, retrieval, and resolution. All three assume the fact came out of a conversation. Some of the things an agent believes about you were never in any conversation. They came from the bookkeeping. ...

Four Memory Types, One Trust Problem Your agent greets a returning user with, “Welcome back — still looking for gardening gifts for your mom?” The user replies, “Actually, she’s more into cooking now.” Your agent now has one well-established memory (“mom likes gardening”) and one fresh claim (“mom likes cooking”), and about half a second to decide which one to keep, which to archive, and whether to trust the new one at all. ...

Every kirana store has a billing system. If the system was set up for a shop that mostly sells atta, dal, and rice, those items get single, efficient product codes — beep, done. But walk in asking for high-mountain oolong tea or high-altitude quinoa, and the shopkeeper has to punch in each word letter-by-letter from a handwritten label. Slower billing. Higher effort. And the shopkeeper has never stocked half of it, so good luck getting a recommendation. ...