The Puzzle of RAG

If you’re like most people, your knowledge isn’t neatly filed away. It’s scattered—some in your head, some in notes you’ll never find again, and some in the half-memory that you “read something about this once.” We’ve built computers to help with this, but the help is pretty crude. Search engines are blunt instruments. Notes apps, if you’re diligent, just give you more things to forget. The real puzzle is: can AI actually help you turn this mess into something coherent and useful?

That’s the promise behind systems like AnythingLLM. The idea is simple, almost obvious in retrospect: combine a search engine’s ability to dig up relevant facts from your own documents with a language model’s ability to explain things. Retrieval-augmented generation, they call it. Instead of asking a language model to answer questions purely from its internal soup of probabilities—a sort of average of everything it’s ever read—you first feed it snippets from your own notes that might be relevant. Then it uses those as anchors to generate an answer.

It’s not so different from what you’d do yourself. If someone asked you about solar cells (okay stupid example), you might flip through your notes, find a few paragraphs, and then summarize them in your own words. Retrieval-augmented generation does the same thing, just much faster.

This approach fixes one of the worst problems with language models: hallucination. Left to their own devices, LLMs can be like that friend who tells convincing stories that aren’t true. By tying the model’s answers to actual facts from your documents, you keep its imagination on a leash. But the whole thing still depends on the boring part: how good your search is. If the system retrieves the wrong snippets, no amount of eloquent synthesis will save you from nonsense.

There’s an irony here. We built language models to move beyond the limitations of keyword search. Now we’re bolting search back onto them to keep them from going off the rails. It turns out that understanding is both retrieval and synthesis. If you try to write an essay from memory, you risk inventing things. If you only string together quotes from your notes, you get a pile of fragments. The real magic happens in the interplay: retrieval grounds you in facts, synthesis connects them into something meaningful.

But what does it actually mean to have a “second brain” built from your documents? It sounds like an upgrade: instant recall, faster insights. But it also changes your relationship with your own knowledge. If you can always ask your AI and get a synthesized answer, maybe you stop remembering things yourself. Your knowledge moves outside your head, the way people stopped memorizing maps once GPS came along. There’s a risk you start trusting the system too much, even though it still makes mistakes. A tool that’s supposed to help you think can, if you’re not careful, do your thinking for you.

Still, the appeal is hard to deny. If you’re a journalist, you can pull up the right quote from years of interviews in seconds. If you’re a lawyer, you can summarize relevant case law instead of wading through stacks of documents. If you’re a student, you can turn months of scattered notes into a study guide. This isn’t just incremental efficiency; it changes the way you work. Your past self becomes more accessible to your present self, and over time, the whole thing evolves. You can even fine-tune the AI on your own writing style, so it starts to sound like you. At some point, it’s fair to ask: where do you end and the assistant begin?

Of course, making this work in practice isn’t just about throwing a big model at the problem. You have to chunk your documents intelligently, tune your retrieval system, and write prompts that actually get good answers. Sometimes a smaller, focused model does better than the latest monster LLM, as long as it has the right context. The art is in the integration, not just the raw horsepower.

In the end, the real question isn’t technical. It’s about augmentation. Can AI help you make better use of what you already know? Can it find connections you’d miss, like Obsidian’s knowledge graph, or at least save you from drowning in your own notes? When it works, retrieval-augmented generation feels like a step toward a true knowledge partner. But it also shifts the boundary between you and your tools. Like any good technology, it extends you—but it also changes you. In building a second brain, you might find your first one adapting in ways you didn’t expect.