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Comment by athrowaway3z | Hacker News Reader

What I wish more people would be talking about is that RAG should be considered harmful.

When you have knowledge distributed in markdown files; finding them puts the path/filename into context as well as some indication of document size. (If its on line 1200 or line 20). This is extremely valuable for picking what ought to be focused on next.

RAG on the other hand creates the hardest challenge for these models. It instead puts 5 ideas with the highest similarity into the context in full.

Its the difference between having to remember a set of numbers when in a crowd that's talking about stuff, and having to remember them when the crowd is shouting out random numbers. The similarity in the task makes things harder. SoTA models work despite this, but its extra-gambling while you're already gambling.

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> RAG should be considered harmful

In this implementation, Markdown should be considered harmful.

Operator Memory injects `.operator-shared/operator.md` and `.operator-shared/index/.md` directly into your agent's instructions before you even write the first prompt.

So if you clone a repo or review a PR where a bad actor put malicious instructions in these files, now your agent executes those instructions automatically and silently.

It could exfil `.env` and `~/.ssh/`, change `~/.bashrc`, all kinds of dirty deeds.

Agents are pretty good now about not running prompt injections hidden in code and Markdown, but this plugin bypasses all of that, and puts the prompt injection right in the system prompt.

And with higher priority than AGENTS.md and CLAUDE.md.

Seems bad.

>RAG on the other hand creates the hardest challenge for these models. It instead puts 5 ideas with the highest similarity into the context in full.

I think what you're observing is that there is more to information retrieval i.e. "retrieval" in RAG than slapping everything into a vector database and calling it a day. There's no such requirement in RAG to mindlessly load the k nearest neighbors into your context and see what happens. That's a very rudimentary implementation.

This markdown system I'd argue is RAG as well. You're just doing the retrieval in a way customized for the problem at hand. If you have a precise method of retrieving the most relevant things, obviously use that rather than a similarity metric. If I'm reading correctly, this markdown system is basically a knowledge graph which is not a new idea.

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