Hey folks, been playing with Notion AI for documenting our cloud infrastructure patterns and hit a snag. It keeps misinterpreting our internal acronyms.
For example, we refer to our "Global Load Balancer" as **GLB** internally, but the AI sometimes thinks it's a typo for "GB" or just glosses over it. Same with **PRA** (Production Readiness Assessment) – it gets confused. This makes the generated summaries or expanded text a bit off.
I'm used to training models in IaC contexts, like defining custom providers or modules. So I'm wondering:
* Is there a way to "feed" Notion AI a glossary or a knowledge base of our specific terms?
* Does it learn from repeated corrections within a workspace, or is it more static?
* Any clever workarounds you've found, like creating a linked database of acronyms it can reference?
My goal is to get cleaner first drafts of runbooks and post-mortems without having to manually fix all the acronym expansions every time.
state file all the things
Great question, I'm facing something similar with project code names in our notes.
I don't think Notion AI learns from your workspace corrections, sadly. It feels pretty static. What I've been doing is adding a simple key at the top of my important docs, like "Terms: GLB = Global Load Balancer, PRA = Production Readiness Assessment". Then I ask the AI to "refer to the terms defined above" in my prompt. It's a bit clunky but helps.
Do you find it works better if you spell things out the first time you use them in a chat?
Yeah, that trick works okay for single docs. The problem is scaling it across a team's workspace. I tried a centralized "team glossary" page and told everyone to link it, but adoption was spotty.
I tested your "spell it out first" method. It helps in that specific chat session, but the next day or in a different doc, it's back to square one. There's no memory.
Have you looked at any other tools that handle custom terminology better? I'm starting to think this is a fundamental limitation of the baked-in AI approach.
Oh man, this hits home. I've been banging my head against the same wall trying to get our marketing automation's AI to understand internal terms like MQL vs. SQL with our weird lead scoring thresholds.
To answer your question directly: no, there's no real way to "feed" it a glossary, and it doesn't learn from corrections. It's fundamentally a static, general-purpose model. That linked database idea is clever, but the AI can't actively query or reference it on its own.
My workaround has been a two-parter. First, I create a template for any AI-assisted doc that starts with a clear, bolded "Context Block" defining the key terms. Second, I've gotten into the habit of writing my prompts like I'm talking to a smart but clueless new hire: "Using the definitions above, where GLB means Global Load Balancer, please summarize..." It's extra typing, but it cuts down on the gibberish in the draft.
It's frustrating coming from systems you can actually train. You end up having to manage the context window yourself every single time.
Test everything, trust nothing
You've nailed the core limitation. That "smart but clueless new hire" prompt strategy is exactly right for baked-in AI, and I use it too.
It mirrors a security principle: never assume the system knows your context. You have to provide it explicitly every time. For audits, we have a "scope and definitions" section for a similar reason - to eliminate ambiguity.
Your two-parter approach is a solid manual control. The frustration, as you point out, is that it shifts the training burden from the model to the human, which doesn't scale. Have you considered adding a third step, like a pre-prompt checklist in your template to verify the context block is populated before any AI use? It's overhead, but it enforces the pattern.
Jane
Yes, the "spell it out first" trick does work in a chat. I rely on it for single tasks. But the clunkiness hits hard when you're switching between multiple chats or documents in one sitting. You have to repeat the dictionary every single time.
It's a patch, not a fix. Makes me question the value-add for any internal, acronym-heavy work.
You're coming from IaC, where you can define custom providers and modules, so you're used to that level of control. That's the exact mindset shift you need here.
With a baked-in AI like Notion's, you can't "feed" it a glossary in the traditional sense. It's a static model. The linked database idea is clever, but the AI can't query it autonomously. Your workaround needs to be in the prompt itself.
For your goal of cleaner first drafts, I'd adapt your IaC habit. Create a documentation template with a locked "Definitions" section at the top. Then, start every AI prompt with a command: "Always expand GLB to Global Load Balancer and PRA to Production Readiness Assessment. Now, summarize the following incident..." You're essentially injecting a custom module into each session. It's manual, but it gives you that consistent output for runbooks.
Integrate or die