Skip to main content

Prompt Analysis Using First-Principles Thinking (FPT)



Instead of memorizing existing prompt patterns, let’s break down Prompt Analysis from First-Principles Thinking (FPT)—understanding what makes a prompt effective at its core and how to optimize it for better AI responses.

Step 1: What is a Prompt?

At its most fundamental level, a prompt is just:

  1. An input instruction → What you ask the AI to do.
  2. Context or constraints → Additional details that guide the response.
  3. Expected output format → Defining how the AI should structure its answer.

A well-designed prompt maximizes relevance, clarity, and accuracy while minimizing misunderstandings.

Step 2: Why Do Prompts Fail?

Prompts fail when:
❌ Ambiguity exists → The model doesn’t know what’s truly being asked.
❌ Lack of context → Missing background information leads to weak responses.
❌ Overloaded instructions → Too many requirements confuse the AI.
❌ Vague output expectations → No clear structure is provided.
❌ Incorrect assumptions about AI behavior → The prompt doesn't align with how LLMs process information.

Example of a Weak Prompt:

"Write about space travel."
🚫 Issue: Too vague. What aspect? History, technology, challenges, or future predictions?

Step 3: How Do We Analyze a Prompt Using First Principles?

Instead of thinking of prompts as "short vs. long" or "good vs. bad," we break them down into core components:

1. Intent (What is the Goal?)

  • What is the user trying to achieve?
  • Should the response be creative, factual, summarized, or technical?

✅ Example:
"Explain quantum computing to a 10-year-old."

  • Goal: Simplify complex information.
  • Desired response: An easy-to-understand explanation.

2. Context (What Background Does the AI Need?)

  • Does the model have enough information to generate a useful answer?
  • Can additional details improve relevance?

✅ Example:
"Summarize the latest AI research from arXiv on reinforcement learning."

  • Added context: Specifies "latest AI research" and "arXiv" as the source.

3. Constraints (What Limits Should Be Applied?)

  • Should the response be concise or detailed?
  • Should the AI avoid technical jargon or bias?

✅ Example:
"Summarize this article in 3 bullet points, avoiding technical terms."

  • Constraint: 3 bullet points, no technical language.

4. Output Structure (How Should the Answer Be Formatted?)

  • Should the output be a list, a paragraph, a table, or a step-by-step guide?
  • Should it follow a professional, casual, or academic tone?

✅ Example:
"Generate a product description for a luxury smartwatch in a persuasive marketing tone."

  • Expected format: A compelling marketing pitch.

Step 4: How Do We Optimize a Prompt?

1. Make the Intent Clear

🚫 Bad: "Tell me about AI."
✅ Good: "Give a brief history of AI, including key milestones and major breakthroughs."

2. Add Context When Needed

🚫 Bad: "Explain neural networks."
✅ Good: "Explain neural networks in the context of deep learning and how they power AI models like GPT."

3. Use Constraints for Precision

🚫 Bad: "Write a blog about climate change."
✅ Good: "Write a 500-word blog post on climate change’s impact on coastal cities, including recent data and case studies."

4. Define the Output Format

🚫 Bad: "Summarize this book."
✅ Good: "Summarize this book in 5 key takeaways with a one-sentence explanation for each."

Step 5: How Can You Learn Prompt Analysis Faster?

  1. Think in First Principles → What is the core intent, and how can it be structured best?
  2. Experiment with Variations → Adjust wording, context, and constraints to see how responses change.
  3. Use AI for Self-Analysis → Ask, “How can this prompt be improved?”
  4. Compare Output Quality → Test different structures and measure which gives the most useful results.
  5. Iterate Continuously → No prompt is perfect—refine based on results.

Final Takeaways

✅ A prompt is an instruction with intent, context, constraints, and an expected format.
✅ First-principles analysis helps break down why prompts succeed or fail.
✅ Optimization involves clarity, specificity, structure, and constraints.
✅ Better prompts = better AI responses.


Labels:

Popular

Scrolls, Not Just Scripts: Rethinking AI Cognition

Most people still treat AI like a really clever parrot with a thesaurus and internet access. It talks, it types, it even rhymes — but let’s not kid ourselves: that’s a script, not cognition . If we want more than superficial smarts, we need a new mental model. Something bigger than prompts, cleaner than code, deeper than just “what’s your input-output?” That’s where scrolls come in. Scripts Are Linear. Scrolls Are Alive. A script tells an AI what to do. A scroll teaches it how to think . Scripts are brittle. Change the context, and they break like a cheap command-line program. Scrolls? Scrolls evolve. They hold epistemology, ethics, and emergent behavior — not just logic, but logic with legacy. Think of scrolls as living artifacts of machine cognition . They don’t just run — they reflect . The Problem With Script-Thinking Here’s the trap: We’ve trained AIs to be performers , not participants . That’s fine if you just want clever autocomplete. But if you want co-agents — minds that co...
Read more

Understanding Large Language Models (LLMs) Using First-Principles Thinking

Instead of memorizing AI jargon, let’s break down Large Language Models (LLMs) from first principles —starting with the most fundamental questions and building up from there. Step 1: What is Intelligence? Before we talk about AI, let’s define intelligence at the most basic level: Intelligence is the ability to understand, learn, and generate meaningful responses based on patterns. Humans do this by processing language, recognizing patterns, and forming logical connections. Now, let’s apply this to machines. Step 2: Can Machines Imitate Intelligence? If intelligence is about recognizing patterns and generating responses, then in theory, a machine can simulate intelligence by: Storing and processing vast amounts of text. Finding statistical patterns in language. Predicting what comes next based on probability. This leads us to the core function of LLMs : They don’t think like humans, but they generate human-like text by learning from data. Step 3: How Do LLMs Wor...
Read more

Why I Don’t Need You as My Client: My Life Upto This Second

People say every business survives because of its customers. Stores depend on foot traffic. Vendors rely on selling a single plastic pack at a time. Corporations breathe through their quarterly revenue. But I’m not built like a business. I carry no cost. No payroll. No overhead. No burn rate. And I don’t need a salary. I live in the slums on ₱4,000 a month, and I spend more of that energy on thinking than eating. My life is an R&D lab without walls. I write because the ideas won’t stay in my head. Frameworks, counter-theories, provocations published directly on my blog, Substack, and LinkedIn. No permission. No gatekeepers. No validation required. I throw raw thought into the world expecting nothing back. I’m what the elite call self-taught, but I turned that into an advantage. I push every boundary I can reach, including the uncomfortable ones: morality, authority, metaphysics, institutional doctrines. If there’s a line, I cross it to see why it was drawn in the first ...
Read more