# Building an LLM-Powered Knowledge Base: A Comprehensive Guide
A knowledge base is a concept where you store a large volume of information and make it accessible for future use. This approach is incredibly powerful for several key reasons: it enables better decision-making, allows you to quickly pick up on past context, and helps align your team around shared information.
Lately, there has been a growing focus on setting up knowledge bases and routing as much context as possible into them to improve all of the above points. Knowledge bases were always useful, even before large language models (LLMs), because accessing past knowledge has always been valuable. However, knowledge bases have grown exponentially more powerful because of LLMs, and there are two main reasons for this. First, you can capture significantly more information in the knowledge bases. Second, you can more easily query the knowledge base — you no longer have to look through it manually.
This article covers why you should set up your own LLM-powered knowledge base, how to capture as much information as possible, and how to actively use the knowledge base.
The topic of knowledge bases has gained considerable traction, with notable examples including the president of Y Combinator building GBrain and Andrej Karpathy building an LLM wiki — both of which are examples of knowledge bases in practice.
There is, of course, no ground truth for the optimal way to build a knowledge base. The most important thing is to actually start storing all of your context into a knowledge base and figuring out how to query the knowledge base effectively at all times — for example, when writing code, in meetings, or in similar situations.
## Why You Should Have a Knowledge Base
You can have different types of knowledge bases. For example, you can have a personal one consisting of all the context that you have personally, or you can have a company-wide knowledge base consisting of knowledge or context that the company possesses.
The reason you should have a knowledge base is that information is extremely valuable. The more information you can store and then later access when needed, the better you will perform. You will, for example, be able to make better decisions because you have access to more context, more quickly pick up on previous topics without having to look through a variety of different sources to find the information you had on the topic, and align different people together because they have a single source of truth.
The same concepts apply whether you have a personal knowledge base or a company-wide knowledge base. These knowledge bases have also become far more powerful because you can query them with LLMs. Previously, you would have had to manually look through the knowledge base to find relevant information. You would have to use your own memory to recall if a certain piece of information was stored in the knowledge base and then decide whether to spend time finding that information or not.
Now that is completely turned around. The LLM can itself query the knowledge base, for example, with a RAG-type approach, and automatically find relevant information immediately. The LLM can itself decide when it needs to use the knowledge base. In other words, you completely remove the human-in-the-loop requirement to access information on a knowledge base, which makes it so much more powerful.
## Capturing Information into the Knowledge Base
The first step of the knowledge base is, of course, to capture information into it. Depending on how your knowledge base is built up, this can happen in a variety of different ways.
However, the first thing to consider is to think of all the different sources of information that you have access to, either personally or at the company. These are, for example, meetings, your project management tool such as Linear, your coding agent such as Claude Code or Codex (including what you have been working on lately with these models and which tasks are completed), and physical office discussions.
You can probably think of a lot of different other sources of information. Of course, this depends a bit on how you work and where you work. The point is that you should map out all these different information sources, and you should figure out an automatic way to route information from these sources into your knowledge base.
It is important that you fully automate the routing of information from the source to the knowledge base. If you require a manual step — for example, pasting meeting notes into the knowledge base — you will definitely forget about it and lose important context, which goes against the entire concept of the knowledge base. The whole point of the knowledge base is that you store absolutely all information there and do not leave anything out. That is what makes a knowledge base so powerful.
For example, with meeting notes, you can have a cron job that syncs daily. It takes each meeting note that everyone in the company has had or that you have had personally and stores it in a knowledge base. You can set up a similar cron job for your Linear or project management tool to sync everything that happened there. Sync your coding agent with what you have been working on and anything you have discussed with your coding agent, and so on. All of this can easily be synced into the knowledge base with a daily cron job.
Physical office discussions are a point that is harder to fully automate. This has not been fully solved yet, but two options would be to record everything going on all the time (which would, of course, require consent) or to manually write down things after having a discussion.
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*This article is based on the original post: [LLM Powered Knowledge Base](https://contributor.insightmediagroup.io/wp-content/uploads/2026/06/llm-knowledge-base_infographic-683×1024.png).*# Building and Utilizing a Knowledge Base for Engineering Work
## The Challenge of Capturing Daily Context
One of the most significant hurdles in building an effective knowledge base is capturing all the information you encounter throughout your workday. This includes conversations with colleagues, decisions made during meetings, and context gathered from various interactions.
However, it’s worth noting that you might not even need to explicitly store every office discussion. In many cases, after having a physical discussion in the office, either you or the person you spoke with will take the context from that conversation and feed it into a coding agent. Since these discussions typically arise from implementation questions, if that knowledge is actively used in your coding agent afterward, you can retrieve it from the coding agent logs.
If you successfully complete this step and store all the context you encounter daily into your knowledge base, you’ve already done most of the heavy lifting. This is the hard part about building a knowledge base. The next step — actively using that stored information when making decisions or interacting with coding agents — is comparatively easier.
## Utilizing Information from the Knowledge Base
Once you have a synced knowledge base containing all the information you need, you can begin actively utilizing it. There are two main approaches to leveraging information from a knowledge base:
1. **Direct querying** — You can simply query the knowledge base when you have a question. This should be done through your coding agent. You ask it a question, and it should know to query the knowledge base to find the answer.
2. **Passive utilization** — The second approach is to have the coding agent passively utilize the knowledge base whenever it performs work.
The first application is fairly straightforward: just ask the question whenever you’re unsure about something. The second approach, however, deserves more attention.
### Passive Utilization of the Knowledge Base
Having the coding agent passively utilize the knowledge base whenever it does work — whether that’s implementing code, fixing bugs, or performing other tasks — is very powerful. There are two main approaches to achieving this:
#### Grep-Based Inference
One approach is to maintain a top-level markdown file in the knowledge base that explains the entire knowledge base and maps out where different pieces of information are stored. This file is updated whenever you add new information to the knowledge base.
The advantage of this approach is that you’re using grep, which is generally more powerful than embedding-based search because it’s better at finding the correct information when needed. However, this also requires you to keep that markdown file in the context of the LLM you’re using at all times. This markdown file can grow quite large, which can become a problem over time.
#### Embedding-Based Inference
The second way to actively use the knowledge base is through embedding-based inference. This is what GBrain is designed for. Essentially, whenever you run a query, an embedding search (similar to a RAG approach) is performed against the knowledge base, and it fetches relevant chunks. If the LLM determines that relevant information has been retrieved through the embedding search, it can then look further into the relevant files.
This is likely the better approach for using the knowledge base during inference because it doesn’t require an active search, and it doesn’t demand spending a large number of input tokens on the knowledge base for every task you perform.
That said, which approach works best will depend on your specific use cases.
## Conclusion
In summary, here are the key steps to follow:
1. Try to set up a knowledge base
2. Write as much information into it as possible
3. Read about how others have set up their knowledge bases
4. Try to set it up yourself
Once established, you should actively use this knowledge base whenever you do work on your computer using a coding agent (which should essentially cover all the work you do). Knowledge bases will become incredibly powerful and valuable in the years to come, and they can also give you a competitive moat because having access to a wealth of information will be a definitive advantage in the future. Furthermore, this data is specific to your company or your personal context — information that, in many cases, only you have access to. If you don’t store it, you’ll never be able to access that information again.
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*Original article source: [Building a Knowledge Base for Engineering Work](https://example.com)*
