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Executive Assistant Advanced ChatGPT cheat sheet
Customize ChatGPT to work the way you do
power user prompt engineering modular AI architecture
Personalize GPTs Remember to architect your work to match the model and instruction. Use faster LLMs for speed, reasoning models for depth and combine outputs when needed.
modular AI architecture screen demo
Modular AI Architecture Personalize and create context blocks. Level of context drives the output, so the input is critical to framing the outcome. Core Ladder Framework # = Top-level instruction (the main “rung” of the ladder). ## = Sub-instruction (a step that supports the main instruction). ### = Further detail, refinement, or constraint (optional, depending on complexity). The more hashes, the deeper you go into the hierarchy. This provides both clarity and structure, letting the LLM interpret your intent without ambiguity. Contact Blocking Separating different parts of your instructions into clear, bounded sections, so the model doesn’t blur them together. How it works: Use visual separators like lines (---), hashtags, or labeled blocks. Place reference content [i.e. a document, dataset, or transcript] in one block. Place instructions in a separate block and add an output format block at the end. This helps the model know what’s “background” versus what’s “task.” Why It’s Effective Visual hierarchy: You control the structure of the chat. Modularity: Easy to add, remove, or edit “rungs.” Iterative refinement: The ladder guides the framework. Model parsing: LLMs handle bullet-style, structured text better than cluttered ambiguous instructions. Why it matters Understand when to pick reasoning versus standard inputs depends on when you want to think deeply or the need to invoke an instruction for a quick answer.
When information and instructions are architected with GPTs, the tool becomes an engine that captures nuance, raises velocity, speeds up decisions, and strengthens execution.
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