Semantic Prompt Engineering 2
When More Words Hurt: How Over-Explaining Breaks Prompt Focus
If you're like most people, you were taught:
"If the AI doesn't understand, just explain more."
And then you wrote a longer, more detailed prompt — only to get an even worse answer.
Why?
Because more words don't always create more clarity.
Sometimes, they destroy it.
π§ The Hidden Problem: Semantic Overload
AI systems don't read prompts like humans following a story.
They scan for meaning hooks — pressure points where they can "collapse" into an answer.
When you over-explain, you often create too many conflicting hooks.
Instead of guiding the AI toward one clear collapse, you scatter its attention across too many competing directions.
Result?
-
Vague summaries
-
Repetitive answers
-
Wrong focus
-
Flat, generic responses
It's like giving a hiker ten different trail maps and saying "Choose one."
π Example: See It in Action
Weak prompt (overloaded):
"Tell me about leadership in modern companies, considering both traditional hierarchical structures and emerging flat organizations, with attention to cultural variations across different countries and potential technology impacts such as remote work dynamics."
Whoa. That's five essays, not one.
The AI is overwhelmed.
Which part should it prioritize?
Hierarchy? Flat structures? Culture? Tech? Remote work?
It can't decide — so it collapses shallowly across everything.
Better prompt (focused):
"Describe two major differences between traditional leadership styles and flat organizational leadership."
Simple. Two directions. Clear contrast.
The AI locks onto the collapse structure you built.
Result: a focused, usable answer.
✂️ How to Trim Without Losing Power
When you feel tempted to "explain more" in a prompt, do this instead:
-
Find the Core:
What one concept do you truly care about? -
Set Clear Boundaries:
What’s inside the question — and what's out? -
Cut Supporting Info:
Remove anything that doesn't serve the collapse focus. -
Sequence if Needed:
If you have lots of angles, break them into separate prompts.
𧩠Pro Tip: Use Two-Layer Prompts
If you must include extra context, separate it cleanly:
-
Layer 1: Set up background in one simple sentence.
-
Layer 2: Give the actual task in a second, clear sentence.
Example:
"In recent years, remote work has changed traditional team dynamics.
List three new leadership challenges created by remote work."
This way, background gently shapes the field, but the collapse focus stays sharp.
π Quick Practice: Simplify This Prompt
Original:
"Discuss how leadership, management, and communication styles differ between startups and large corporations, including factors such as team size, funding stability, innovation speed, and customer feedback loops."
π Simplified Version:
"Explain two key differences between leadership styles in startups and large corporations."
(If you want more later, ask in a second prompt.)
✨ Takeaway:
The goal of a prompt is not to say everything.
The goal is to collapse the right meaning, fast.
When you explain too much, you don’t clarify —
you blur the map.
✅ Keep your prompts sharp.
✅ Build strong, simple hooks.
✅ Save the extra layers for later.
Semantic Prompt Engineering - Full Series
Semantic Prompt Engineering 1: The Secret Behind Great Prompts: Finding the Real Meaning Hooks
Semantic Prompt Engineering 2: When More Words Hurt: How Over-Explaining Breaks Prompt Focus
Semantic Prompt Engineering 3: Tiny Tweaks, Big Wins: How a Single Line Can Sharpen AI Responses
Semantic Prompt Engineering 4: The Loop Trap: Why Repetitive Prompts Confuse AI and How to Fix It
Semantic Prompt Engineering 5: Setting the Scene: Role and Context Framing for Better AI Alignment
Semantic Prompt Engineering 8: Guiding Without Pushing: How to Lead AI Through Background Cues
Semantic Prompt Engineering 9: Tune the Rhythm: How Prompt Flow and Pacing Affect AI Understanding
Semantic Prompt Engineering : Master Summary and Closing Tips: Becoming a True Meaning Engineer
© 2025 Danny Yeung. All rights reserved. ηζζζ δΈεΎθ½¬θ½½
Disclaimer
This book is the product of a collaboration between the author and OpenAI's GPT-4o language model. While every effort has been made to ensure accuracy, clarity, and insight, the content is generated with the assistance of artificial intelligence and may contain factual, interpretive, or mathematical errors. Readers are encouraged to approach the ideas with critical thinking and to consult primary scientific literature where appropriate.
This work is speculative, interdisciplinary, and exploratory in nature. It bridges metaphysics, physics, and organizational theory to propose a novel conceptual framework—not a definitive scientific theory. As such, it invites dialogue, challenge, and refinement.
I am merely a midwife of knowledge.
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