How to build an AI knowledge base

What is an AI knowledge base?

An AI knowledge base is a structured collection of information that is created, maintained, and enhanced by an AI agent without the need for a human author.

Unlike traditional knowledge bases or wikis, where people write and organize every entry by hand, an AI knowledge base uses an agent to ingest raw sources (articles, conversations, documents, data), summarize them, identify relationships between topics, and organize everything into a navigable, cross-linked structure.

The result is a living reference that grows and improves over time as new information is added and the AI agent refines its organization.

Human readers benefit from a system that connects dots across sources, surfaces what matters in the moment, and turns information into clear, usable insight they can act on.

From knowledge management to knowledge creation with AI

Andrej Karpathy, the researcher who coined "vibe coding" a year ago, recently posted a tweet that caught the attention of 4 million people. He's spending more tokens directing his AI agent to build knowledge bases than write code.

His setup is elegant in its simplicity. Raw sources (articles, papers, repos, datasets, images) go into a folder. Then an agent compiles them into a wiki of summaries and concept articles, all cross-linked.

Karpathy views and navigates everything in Obsidian, a notetaking app that lives locally on his computer. The AI writes and maintains all of the data, and Karpathy rarely touches it directly.

Once his wiki reached about 100 articles and 400,000 words, he was able to ask the AI agent complex questions about the knowledge base and get substantive, grounded answers. How? The AI auto-maintains index files and summaries, which helps it to retrieve answers.

The most powerful part of Karpathy’s workflow is the compounding loop. When he queries the knowledge base, the answers get filed back into the wiki. So, every exploration makes the knowledge base better.

Thousands of people tweeted questions or examples of their own setup. For instance, popular technology podcaster Lex Fridman showcased a similar workflow for his guest research, using Obsidian and Cursor, a coding app.

Why traditional knowledge base software falls short

Karpathy’s setup is single-player by design. He manually adds each source, only he can prompt the agent, and the workflow runs privately on his local machine. For one person, that kind of knowledge base compounds extremely well.

But businesses need a multi-player knowledge base that can serve many people at once.

Traditional knowledge base products are built around structured documents, and struggle with the messier forms of content teams produce. That includes the chats, working notes, and fragments of context scattered across tools. No one person can track or organize all of it.

The result is that the most important institutional knowledge is often the hardest to recover. Key decisions and the reasoning behind them are usually buried in a Slack thread that nobody will ever go back and find.

The solution to this problem is a knowledge base built for teams, and maintained by AI.

What teams need from AI knowledge base software

Teams need three capabilities from an AI knowledge base.

Collaboration. Multiple people contributing knowledge without anyone manually filing sources. The team's normal work, their conversations, documents, and decisions, is the raw input. Nobody has to change how they work.

Automation. The knowledge base should grow on a schedule, not when someone remembers to run a script on their laptop. If it depends on one person being at their desk, it will die within a week.

A purpose-built UX. Not a terminal window. Not a directory of markdown files that only the person who set it up can navigate. Something the whole team can browse, search, and reference without touching a command line.

How Adapt built an AI knowledge base from Slack

We ran an experiment with our own marketing channel in Slack where Adapt staff and outside consultants collaborate.

Every day there is a new thread on content, competitive research, SEO strategy, event planning, and product marketing.

Keeping everyone aligned on what was discussed had become messy, so it was the perfect use case for an AI knowledge base.

We asked Adapt to build a knowledge base app for the channel. Here is what it did:

• It read the last seven days of conversations, including every thread and reply.
• It summarized each conversation into a structured entry with participants, tags, key decisions, and current status.
• It cross-linked entries where topics overlap.
• It deployed the project as a persistent, browsable web app with search and filtering.
• And it scheduled a daily update at midnight that reads new conversations, processes them into new entries, wires cross-links to existing entries, and rebuilds the app.

We can't show you the real data, but we had Adapt build a mock app so you can get the feel of what's possible.

How AI agents turn knowledge management into a compounding loop

Karpathy describes the best part of his personal workflow:

"Often, I end up filing the outputs back into the wiki to enhance it. So my own explorations and queries always add up in the knowledge base."

That compounding loop happens naturally in a team knowledge base. When someone asks a question in the channel and Adapt can answer using context it already gathered from channel’s history. The graph becomes more useful every day without any human curating it.

Build your own AI knowledge base in 60 seconds

You can build this for any shared channel in about 60 seconds. Ask Adapt to create a knowledge base app that summarizes, organizes, and cross-links your conversations, and updates it once a day on a schedule.

Karpathy is right that there is room for an incredible new product. We think it starts with making knowledge bases collaborative.

FAQ

What is an AI knowledge base?

An AI knowledge base is a structured collection of information that is created, organized, and maintained by a large language model. Instead of humans writing every entry, an LLM ingests raw sources, summarizes them, identifies relationships, and organizes everything into a cross-linked, searchable structure.

How is an AI knowledge base different from RAG?

Retrieval-augmented generation (RAG) retrieves relevant chunks of text at query time and feeds them to an LLM for an answer. An AI knowledge base is a persistent, organized structure that the LLM builds and maintains over time. RAG is a retrieval technique. An AI knowledge base is a knowledge artifact. Karpathy found that a well-organized knowledge base with auto-maintained index files can replace fancy RAG pipelines entirely.

Can an LLM replace a wiki?

For knowledge that can be derived from existing sources (conversation summaries, research synthesis, decision logs), yes. LLMs are excellent at summarizing, organizing, and cross-linking information. For knowledge that requires original human expertise or judgment, the LLM serves as an organizer and the human remains the author.

How do you build a knowledge base from Slack conversations?

Connect Adapt to your Slack workspace. Ask it to read a channel's history, summarize conversations into structured entries, cross-link related topics, and deploy the result as a browsable app. Then schedule a daily update to keep it growing automatically.

Is an AI knowledge base useful for small teams?

Especially useful for small teams. Small teams generate a high volume of cross-functional conversation relative to their ability to document it. A five-person team working across marketing, product, and engineering in the same Slack channel can accumulate dozens of decisions per week that nobody writes down. An AI knowledge base captures all of it.