Original Insight
“You want to reframe from a general question like ‘what is X?’ to being explicit about the context, which is, ‘according to the context, what is described as X?’ Instead of ‘how does X work,’ it’s ‘based on the information provided, how is X described as working?’ Why does X? According to the document, based on the context, according — that’s the whole idea. It is a little bit intended to reduce hallucinations, but more importantly, it’s intended to make sure that, without creating a rag, you can just make sure that for example, if you’re in the middle of a conversation and you want to do a web search, and you only want the response to come from the results of the web search, not from your entire conversation, then you can use this grounded command.” — Lou
Expanded Synthesis
Lou introduced the “grounded” command in the February 12th session — a slash command for Claude Code that forces the AI to answer strictly from a specified context. The context can be a file path, a glob pattern, a URL, or a live web search, and the response is bound to that source: what was found, what wasn’t found, and the explicit sources used.
The mechanics are specific, but the principle underneath is broadly applicable and more significant than it might first appear. What Lou identified is a fundamental design flaw in how most people interact with AI: they ask general questions into a conversational context that has accumulated many layers of prior exchange, training-data assumptions, and inference by analogy. The model answers from the totality of everything it knows and everything in the conversation, weighed by probability. This produces answers that feel fluent and confident — but the confidence is often distributed across many sources of varying relevance and reliability, not anchored to any one of them.
The grounded approach inverts this. Instead of asking “what is X?”, you ask “according to [this specific source], what is X?” The constraint is not a limitation — it is a precision instrument. It transforms an AI from a generalist advisor drawing on everything it has ever learned into a focused analyst working within a defined evidentiary boundary.
For coaches and knowledge entrepreneurs, this principle has several powerful applications that go well beyond reducing hallucinations (though that is a genuine benefit). The first is intellectual honesty in content creation. If you are writing about a framework or a study or a client case, grounding your query in the actual source material ensures that your content represents what is actually there, not a blended approximation of related things. This is particularly important for coaches who are building authority: your intellectual credibility depends on the precision of your claims, and precision requires that you know exactly where your claims come from.
The second application is in coaching sessions themselves. When a client presents a situation and asks for frameworks or analysis, a grounded approach means working from what the client has actually said and the specific context they’ve provided — not from your accumulated mental model of “clients like this” or generic coaching frameworks. The grounded query is, in essence, a form of deep listening operationalized as a method.
The third application is in learning and research. When you encounter a new framework, book, or article and want to extract its core principles, grounding your queries in the specific text ensures that you’re learning what the author actually said, not a probabilistic summary of the category the text belongs to. This is how you build genuine intellectual capital rather than a blurring average of everything you’ve consumed.
The conversation that followed Lou’s demonstration also surfaced an important nuance: grounding doesn’t mean uncritical acceptance of the context. Bally’s question about whether the grounded command was “evidence-based” points to the right orientation — grounding specifies the evidentiary source, not the authority of that source. You can be grounded in a comedy routine (Lou’s example) or in a partisan opinion piece. The grounded query gives you clean extraction; the critical evaluation of the source is still your job.
This distinction matters enormously for high-performers who are building AI-assisted knowledge systems. The ability to extract cleanly from a source is necessary but not sufficient. The meta-skill is knowing which sources deserve to anchor your thinking and which sources, while quotable, should be held at arm’s length. AI is excellent at the former; you remain responsible for the latter.
There is also a momentum dimension here that connects to PowerUp themes. High-performers often stall not because they lack information but because they have too much of it, too loosely organized, and the AI keeps giving them synthesized blends rather than precise answers to precise questions. Grounding is a practice of deliberate narrowing — choosing one source, one context, one evidentiary boundary — that breaks the paralysis of endless synthesis. It creates forward motion by enforcing commitment to a specific frame.
Practical Application for PowerUp Clients
The Grounded Query Practice
You don’t need the slash command to apply this principle. It is a question-framing discipline applicable in any AI conversation and in coaching work.
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Before asking any AI question, identify the source. What specific piece of content, conversation, or research do you want the answer to come from? Name it explicitly in your prompt. “Based on this transcript…” “According to this article…” “From what we’ve discussed in this session…”
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Use context-anchored language. Replace “what is X?” with “according to [source], what is described as X?” Replace “how does X work?” with “based on [source], how is X described as working?” This shift is not just stylistic — it changes what the model attends to.
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Ask what wasn’t answered. The grounded format explicitly surfaces what the source didn’t address. Train yourself to notice the gaps — they are often the most important signal. What your current frameworks and sources don’t address is precisely where your original thinking can live.
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Apply to coaching sessions. Before offering a framework or interpretation in response to a client’s situation, ground your response: “Based on what you’ve just shared…” “From what you’ve told me about this pattern…” This makes your coaching demonstrably responsive to the client’s specific reality rather than your generic model.
For Content Creation: Take any article you’re planning to write and identify the single primary source — one piece of research, one interview, one framework — that will anchor the piece. Write from that source first. Then, in revision, you can layer in supporting context. This prevents the blurring synthesis that makes most AI-assisted content feel generic.
Coaching Questions:
- “What’s the actual evidence for that conclusion? Where did it come from?”
- “If we set aside what you’ve read and heard from others — what does your direct experience tell you?”
- “What question would you ask if you only had this one piece of information to work from?”
Additional Resources
- The Checklist Manifesto by Atul Gawande — the discipline of specifying constraints as a quality-control mechanism
- Being Wrong by Kathryn Schulz — the psychological dynamics of confidence detached from evidence
- Insight - Codify Your Judgment Into Skills, Not Just Prompts — once the grounded query practice becomes a discipline, turn it into a repeatable prompt or skill
Evolution Across Sessions
Lou’s grounded command connects to the multi-model approach he demonstrated on February 26th, where he used Claude, Gemini, and Codex in parallel, each contributing to a shared markdown file. That setup is, in effect, a multi-source grounded query: each model is grounded in the same file, contributing from its own angle, and the synthesis happens at the end with explicit attribution of which model contributed which ideas. The grounded principle scales up to team-level AI collaboration.
Next Actions
- For Lou: Consider teaching the grounded query discipline as a foundational prompt engineering practice in the mastermind — it addresses the hallucination concern while also building the precision habit that good thought leadership requires.
- For clients: For the next two weeks, add a source anchor to every significant AI query. Track whether the responses are more or less useful as a result. Reflect on what the discipline reveals about where your questions have been vague.