Zettelgarden: Building Your Intelligent External Memory
Adding conversational AI to your personal knowledge management system
Following up on my last post about implementing retrieval-augmented generation (RAG) in Zettelgarden, I've been thinking about the next major feature: making the system truly interactive through an intelligent chat interface. While RAG has been great for finding connections between cards, I want to take it further.
Why Add Chat?
Here's the thing about note-taking systems: they're only as good as your ability to retrieve and use the information when you need it. The RAG implementation I discussed in my last post was a huge step forward in automatically finding connections between cards. But what if we could just ask our knowledge base questions directly?
I'm not talking about just another LLM chat interface - we have plenty of those. I'm talking about a chat system that knows your cards, your tasks, and your personal knowledge base. Think of it as having a really smart assistant who has read everything you've ever written in your zettelkasten.
What I'm Building
The core idea is to use an LLM as a smart router for commands and queries. When you ask a question, the system will figure out the best way to answer it by combining different types of searches and actions. Here are some examples of what I want to be able to do:
"Who did I have that meeting with six months ago about the database migration?"
Instead of manually searching through cards, the system would:
Search through meeting cards using RAG
Check task history
Combine the information into a coherent answer
Or something like "I just had a meeting with Sarah about the new frontend design. Can you create a card for it?"
The system would:
Generate a new card with the appropriate ID
Fill in meeting details
Create any relevant follow-up tasks
Link it to existing cards about Sarah or frontend design
Here are some other types of questions you might ask:
"What have I learned about distributed systems in the last year?"
"How has my thinking about microservices evolved since 2022?"
"Based on my meeting notes, what are the recurring challenges in the frontend team?"
"Find contradictions in my notes about software architecture"
Building on RAG
The RAG implementation I described in my last post is actually the foundation for all of this. The vector embeddings we're already generating for cards give us the ability to find semantically related information, not just exact matches. This means when you ask about "that meeting about improving performance," it can find relevant cards even if they don't use those exact words.
Why Zettelgarden?
You might be wondering why build this into Zettelgarden instead of using one of the many AI assistants out there. The key difference is that Zettelgarden is built around the idea of structured note-taking and linking. The graph of connections between cards creates a natural knowledge structure that an LLM can understand and navigate. Unlike general AI assistants, this system will understand your specific context, your projects, and your history. Zettelgarden also makes it easy to capture and structure information.
There is a virtuous cycle within this: the more information you put into Zettelgarden, the better the results will be, so the more information you will want to put into it. For example, when you start documenting your meetings, you'll find it easier to track decisions and follow-ups. This encourages you to document more meetings, which in turn makes the system even better at understanding your work context and relationships. The same applies to your learning notes, project documentation, and personal insights.
Technical Challenges
I'm in the early stages of building this feature, so some details are still uncertain. The biggest challenge will be getting the command routing right - having the LLM correctly understand when to search cards, when to create new ones, when to update tasks, and so on.
I'm also thinking about how to handle complex queries that might require multiple steps. For example, "What were the main outcomes of my meetings with the design team last quarter?" might involve:
Finding all relevant meeting cards
Extracting key points
Summarizing the information
Checking related task completion status
Another challenge will be in linking, the human/computer interface and deciding on a strategy surrounding that. Zettelgarden works best when cards are linked together. There are lots of options for LLMs to link cards directly. The problem with that is if an LLM makes a less than ideal link, it will make future results worse. I think the benefit of the user here is that they can tailor links and the internal graph to their own insights, and that automating too much of the knowledge connections may be a mistake.
The Bigger Picture
This is all part of my vision for Zettelgarden as more than just a note-taking app - it's your external memory system. The combination of structured note-taking, automated connections through RAG, and now intelligent interaction through chat, creates something that feels more like an extension of your own thinking process.
The goal isn't to replace your own thinking or memory, but to augment it. To help you make better use of all the knowledge you've collected and all the thoughts you've recorded.
I'll be posting updates as I build this out. As always, you can check out the progress on GitHub, and I'm looking forward to seeing how this evolves.