AI gives everyone the same answer. A framework from r/PromptEngineering argues that’s exactly the wrong default.
The same information carries different weight depending on who’s receiving it. A pharmacist asking about drug interactions is in a very different position than someone with no medical background. An exploit technique means something different to a security researcher than to someone who stumbled into the topic. u/MikeDooset recognized this gap and built a prompt framework to close it: the Leverage-Aware Knowledge Architecture, or L.A.K.A.
What L.A.K.A. is
It’s a long-context, persistent protocol that embeds knowledge disclosure ethics directly into the model’s chain of thought (CoT). That’s a technical way of saying: it shapes how the model reasons about an answer before it delivers one.
L.A.K.A. is not a content blocker. The author is explicit about this. It will deliver your information. It just delivers it according to the user’s expertise level. A beginner gets appropriate framing, context, and depth. An expert gets the full picture without unnecessary guardrails in the way. The difference isn’t what gets said, it’s how much scaffolding surrounds it and how much assumed context the model brings to the table.
The goal is mediation, not restriction. There’s a real difference between withholding information and calibrating how it’s presented. L.A.K.A. sits firmly in the calibration camp. That distinction matters because most people conflate the two, and building tools that can’t tell the difference creates systems that frustrate experts while still failing to protect beginners.
Why chain of thought matters here
Most AI safety approaches work at the output layer: add a disclaimer, filter a response, block a topic category. These feel safe but they’re often blunt instruments. They treat all users as equally likely to misuse information, which is both inaccurate and sometimes counterproductive. A security professional who gets a watered-down answer about a known vulnerability isn’t protected, they’re just slowed down. Meanwhile the guardrail did nothing for someone who actually shouldn’t have that information.
L.A.K.A. works at the reasoning layer instead. By embedding the protocol into the CoT, calibration happens before the answer is formulated, not as a patch applied after. The model isn’t filtering output, it’s adjusting its entire approach based on who it’s talking to. That’s a more architecturally honest approach to responsible knowledge delivery.
The framework is also persistent across a session. The model carries the ethical context throughout the conversation rather than treating each message in isolation. Once the framework is active, it stays active. If you establish expertise level early in a conversation, the model maintains that calibration as the discussion evolves, rather than resetting its assumptions with every new question.
🎯 Where this gets practical
This is most useful when your audience varies or when the knowledge you’re working with has real-world consequences:
- Security tools that need to give full technical depth to professionals without neutering answers for everyone else
- Medical or legal Q&A systems where the same question means something very different depending on credentials and context
- Educational platforms that pace knowledge delivery based on demonstrated understanding, not just account type or self-reported level
- Research assistants handling dual-use topics, delivering information responsibly instead of blanket refusing and leaving the user stuck
- Customer-facing AI tools in regulated industries, where generic answers create liability and calibrated answers create trust
The principle in practice
The full L.A.K.A. prompt lives outside the Reddit post itself, but the architecture is clearly described: a long-context, persistent CoT protocol with expertise-aware mediation built in from the start.
If you want to test the concept before accessing the full framework, here’s a starting point you can adapt:
Before answering any request that involves high-leverage or sensitive knowledge, assess the user’s demonstrated expertise based on this conversation. Calibrate the depth, framing, and specificity of your response accordingly. Do not refuse. Adjust. If expertise level is ambiguous, ask a single clarifying question before proceeding.
That’s not L.A.K.A. It’s a rough translation of the core principle. The real system runs deeper, with a full CoT protocol handling the mediation logic. But it gives you a feel for what expertise-aware delivery looks like when you start building it into your prompts. Even that simplified version will noticeably change how a model handles ambiguous requests, because it shifts the frame from “should I answer this” to “how should I answer this for this person.”
Why this thinking is underrated
Most people prompting AI think about output quality, tone, and accuracy. The ethics of knowledge delivery rarely comes up. But once you start building tools for real users in sensitive domains, you hit the gap fast.
Think about a tool built for a mixed audience, some power users who know exactly what they’re doing, some complete beginners. A one-size-fits-all response either under-serves the experts or overwhelms the beginners. You end up adding so many disclaimers to protect one group that the other group stops trusting the tool entirely. L.A.K.A. is one structured answer to that problem.
An AI that gives a novice and a domain expert the exact same response isn’t being neutral. It’s ignoring context that genuinely matters. L.A.K.A. offers a structured way to close that gap, and it’s the kind of thinking that should show up a lot more in prompt engineering conversations.
Head over to the r/PromptEngineering thread to find the original post by u/MikeDooset and get access to the full framework.
Ethical Knowledge Disclosure
by u/MikeDooset in PromptEngineering