Your AI results are probably average because you’re asking it to do two incompatible things at once.
I was scrolling through YouTube when I found this absolute goldmine of a tutorial by an AI educator that explains why our prompts fail and how to fix them. The original creator breaks down a phenomenon called “plan abandonment,” which happens when an AI tries to plan a complex strategy and execute it simultaneously. Its attention splits, it gets overwhelmed, and it defaults to the safest, most generic advice found on the internet. To fix this, the expert outlined a three-step workflow that forces the model to stop guessing and start operating at an elite level.
The Core Concept: Separation of Concerns
The big takeaway here is that you cannot rely on the AI’s default training for high-stakes tasks. LLMs are prediction engines designed to find the most likely next word, which inevitably leads to the “average” of human knowledge. The savvy professional behind this video argues that to get expert results, you must separate the process into three distinct phases: gathering the strategy, establishing the context, and synthesizing the instructions. By doing this, you prevent the AI from diluting its focus.
Here is the breakdown of the creator’s system:
📌 The Expert Anchor (Grounding)
The first step is to stop asking the AI for advice and start giving it a brain. The author explains that instead of asking “How do I launch a product?”, you should find a proven framework, like a PDF of a bestseller, a transcript of a lecture, or a research paper, and upload it.
How it works:
You tell the AI to analyze the document and extract the core system, the step-by-step logic, and the specific constraints. The key instruction here is to tell it not to summarize, but to reconstruct the system into a master guide. This process is called “grounding.”
A brilliant tip from the video:
If you don’t know who the experts are in your niche, ask the AI first. But don’t just ask for names. The creator suggests asking for “signature frameworks” and, crucially, “where these experts disagree with each other.” This reveals the nuance that generic advice misses. Once you have the source material, you run the extraction prompt. The result is that your AI is no longer hallucinating average marketing tips; it is simulating the mind of a top-tier industry leader.
📌 Context Extraction (The “Flip Script” Method)
Now that you have the “how” (the expert system), you need the “what” (your specific situation). The problem is that humans are terrible at knowing what context is relevant. We usually type out a paragraph of what we think is important, but we leave out critical details like budget constraints or team size.
The solution:
The innovator behind this method suggests flipping the script. Do not write a context prompt. Instead, tell the AI: “I am working on Project X. Ask me a series of questions one by one to gather all the context you need. Do not move on until I have answered each one.”
Why this matters:
This interview style forces you to dig deep. The AI might ask about your audience demographics or specific technical limitations you hadn’t considered. Once the interview is done, you give one final command: “Compile all my answers into a single structured context file.” You now have a comprehensive dossier on your project that acts as the perfect counterbalance to the expert framework.
📌 Meta-Prompt Synthesis
This is where the magic happens. You have the Expert Anchor (the strategy) and the Context File (the reality). If you just paste them both in and say “go,” the AI might still get confused. The pro tip here is to use a “Meta Prompt,” effectively asking the AI to write the final prompt for itself.
The technique:
The creator uses a specific structure (often called the RICE framework) to merge these documents. He pastes the expert guide and the context file into the chat, utilizing XML tags (like <context> and </context>) to help the AI clearly see the boundaries of the data.
Then, he commands the AI to act as a “Senior Prompt Engineer.” The task is to synthesize the two data blocks into a single master execution prompt. The AI will look at the expert rules, look at your specific answers, and write a highly complex, rigid set of instructions for itself. You take that output, open a fresh chat, and run it. Because the planning was done separately from the execution, the quality of the output is exponentially higher.
Check out the full breakdown and the specific copy-paste prompts in the link below!