Alex Hughes Profile picture
Sep 23 16 tweets 5 min read Read on X
Prompt engineering "experts" are teaching you wrong.

They overcomplicate what's actually dead simple.

I reverse-engineered how the best AI researchers actually prompt.

Here's the complete guide you can follow to become a pro AI user:
WEEK 1-2: STOP BEING VAGUE

Bad prompt: "Help me with marketing"

Good prompt: "Write 5 email subject lines for a project management SaaS launching to small business owners. Make them curiosity-driven and under 50 characters."

See the difference? Specific request, clear audience, exact format. Practice this for 30 minutes daily.
THE CONTEXT-ROLE-TASK-FORMAT FRAMEWORK

This is your new religion. Every prompt should have:

Context: What's the situation?
Role: Who should the AI be?
Task: What exactly do you want?
Format: How should it look?

Example: "You're a startup advisor (role) helping a B2B SaaS with 1000 users (context). Write 3 pricing strategy options (task) as: Strategy name, target customer, price point, reasoning (format)."
WEEK 3-4: MASTER FEW-SHOT PROMPTING

Show don't tell. Give AI 2-3 examples of what you want.

"Write Instagram captions like these:

Example 1: Just launched our new feature. Beta users are calling it 'life-changing.' Sometimes the best validation comes from the people actually using your product.

Example 2: Spent 6 months building something nobody wanted. Painful lesson but necessary. Now I validate ideas in 48 hours, not 6 months.
Now write one about: [your topic]"
I use few-shot for everything. Social posts, emails, code reviews, strategy docs. The AI learns your exact style instead of guessing.

Your outputs become 10x better because you're training the AI on what good looks like for YOU specifically.
WEEK 5-6: CHAIN COMPLEX PROMPTS

Stop trying to do everything in one prompt. Break big tasks into steps.

Prompt 1: "Analyze the project management software market. Focus on: market size, key players, pricing trends, customer pain points."

Prompt 2: "Based on that analysis, identify 3 underserved niches in project management software."

Prompt 3: "For each niche, create a basic product concept with key features and go-to-market strategy."
Chaining lets you handle complex work that would overwhelm a single prompt. Each step builds on the last. It's like having a team of specialists working in sequence.

I use this approach for business plans, technical architectures, content strategies.
THE MEGA PROMPT TEMPLATE

For complex one-shot requests, use this structure:

"ROLE: You are [specific expert]
CONTEXT: [situation, constraints, background]
TASK: [what you want done]
REQUIREMENTS: [specific criteria, must-haves]
FORMAT: [exact structure you want]
EXAMPLES: [show 1-2 if relevant]
CONSTRAINTS: [word count, tone, limitations]"

This template works for everything from technical docs to creative campaigns.
WEEK 7-8: BUILD YOUR PROMPT LIBRARY

Save your best prompts. I have 50+ templates for different scenarios:

1. Client onboarding questions
2. Content brainstorming
3. Code review checklist
4. Strategy analysis
5. Email responses

And more.

Don't reinvent the wheel. Build once, use forever. Your productivity compounds when you stop starting from scratch every time.
WEEK 9-10: ADVANCED TECHNIQUES

1. System instructions: Set permanent context so every chat starts with your preferences
2. Multi-modal: Upload images, PDFs, spreadsheets for analysis
3. Tool integration: Connect AI to Zapier, APIs, custom workflows
4. Iterative refinement: Ask AI to improve its own outputs

Example: "Review your last response. Make it more concise and add specific examples for each point."Image
THE DEBUGGING FRAMEWORK

When prompts fail (they will), use this:

"What information do you need to give a better answer?"
"Break down this task into smaller steps"
"Show me 3 different approaches to this problem"
"What assumptions are you making that might be wrong?"

Most prompt failures happen because of missing context or unclear expectations.
WHAT GOOD PROMPTING UNLOCKS

Content creation: I generate 2 weeks of social posts in 45 minutes
Business analysis: Complex market research in hours, not days
Learning: Master new skills with AI as personal tutor
Decision making: See angles and possibilities I'd miss alone
Time leverage: My thinking time becomes 10x more valuable

The ROI is insane once you get good at this.
Most people quit after a few bad results.

They expect perfect outputs without practicing the skill. Good prompting takes deliberate practice. But unlike other skills, this one has compound returns. Every hour invested returns 10x in productivity gains.

The people who master this early will have an unfair advantage for decades.
YOUR 30-DAY CHALLENGE

Days 1-7: Practice specific, clear requests
Days 8-14: Use Context-Role-Task-Format for everything
Days 15-21: Build few-shot examples for your common tasks
Days 22-30: Start chaining complex prompts

Track your best prompts. Share what works.

This skill will change your career.

The question isn't whether AI will replace jobs. It's whether people who are good at AI will replace people who aren't.
90% of customers expect instant replies.

Most businesses? Still responding days later.

Meet Droxy AI: your 24/7 AI employee:

• Handles calls, chats, comments
• Speaks 95+ languages
• Feels human
• Costs $20/mo

Start automating:

try.droxy.ai/now
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More from @alxnderhughes

Sep 22
OpenAI engineers don't prompt like everyone else.

I reverse-engineered their internal techniques from leaked docs and demos.

The difference is insane.

Here are the 5 insider methods they don't want you to know:
1. Role Assignment

Don't just ask questions. Give the AI a specific role first.

❌ Bad: "How do I price my SaaS?"

✅ Good: "You're a SaaS pricing strategist who's worked with 100+ B2B companies. How should I price my project management tool?"

The AI immediately shifts into expert mode.
Role assignment works because it activates specific training patterns. When you say "you're a copywriter," the AI pulls from copywriting examples, not generic advice.

I use this for everything. Marketing strategy? "You're a CMO." Technical advice? "You're a senior engineer." It's that simple.
Read 14 tweets
Sep 17
Scientists just published the most rigorous test of AI consciousness ever run.

They pushed Claude through experiments that most ethics boards wouldn’t even approve for humans.

What they found flips our understanding of “consciousness.”

Here's the breakdown: Image
Virtual world test:

They built a 4-room virtual environment where Claude could freely explore different types of content. Each room contained 20 letters with different themes.

The question: would Claude show genuine preferences or just random behavior? Image
Claude's revealed preferences:

When given complete freedom, Claude consistently chose certain topics over others. Consciousness, creativity, and understanding dominated its choices.

This wasn't random - it was systematic across multiple runs.
Read 14 tweets
Sep 10
MIT just dropped a report that will define the next decade of business.

"The GenAI Divide" reveals which companies will survive AI disruption.

The data is absolutely brutal for most businesses.

Here's what every leader needs to know → Image
MIT analyzed over 300 AI projects, interviewed 52 organizations, and surveyed 153 senior leaders.

The result?

→ 95% of enterprise AI implementations are failing.
→ Only about 5% of pilots reach production and deliver measurable P&L impact.

Adoption ≠ Transformation. Image
The problem isn’t the models.

It isn’t regulation.

It’s learning.

Most tools don’t retain feedback.
They don’t adapt to workflows.
They don’t get better with use.

So they stall. Image
Read 13 tweets
Sep 2
MIT researchers just found out:

99% of people are prompting wrong.

They throw random words at AI and hope for magic.

Here’s how to actually get consistent, high-quality outputs:
There are 3 main ways to prompt:

👉 Zero-shot
👉 Few-shot
👉 Chain-of-thought

Each works in different scenarios.

Get this wrong, and your outputs will always be shaky. Image
Zero-shot → Just ask the question.

Example:

“Summarize this article in 3 bullet points.”

Good for simple, clear tasks.

But it collapses when the task is fuzzy or creative. Image
Read 14 tweets
Sep 1
Context length is the most important AI concept nobody explains.

It’s literally why your chatbot “forgets.”

Here’s the concept explained in plain English 👇 Image
Every Large Language Model (LLM) has a token limit.

A token = a chunk of text (≈ 3–4 characters of English).

Think of it as the AI’s working memory.

If you exceed it, the model starts dropping information.

Example:

- GPT-4o has ~128k tokens (~300 pages of text).
- Claude 3.5 Sonnet has 200k tokens (~500 pages).
- Gemini 1.5 Pro: 1M+ tokens (~3,000 pages).

But no model has “infinite memory.”Image
Why it matters:

Context length defines how much history you can pass in:

- A long chat log
- A book or research paper
- Multiple files or codebases

If your prompt + conversation > token limit → earlier parts get truncated.
Read 10 tweets
Aug 31
Holy sh*t… Claude with XML is a different beast.

Anthropic researchers quietly dropped the framework — and no one’s talking about it.

It’s like switching from calculator to supercomputer.

Here’s the structure they didn’t put in the docs:
Why XML?

Claude was trained on structured, XML-heavy data like documentation, code, and datasets.

So when you use XML tags in your prompts, you’re literally speaking its native language.

The result? Sharper, cleaner, and more controllable outputs.

(Anthropic says that XML tag prompts gets best results)Image
Why it works:

✅ Clarifies intent
✅ Mimics Claude's training structure
✅ Boosts reasoning + structure
✅ Reduces hallucination

Now let’s dive into 5 🔥 real-world use cases:
Read 11 tweets

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