Stop Reading, Start Learning: My 10 Favourite NotebookLM Prompts for PDFs

I used to spend 45 minutes reading a report cover-to-cover — only to realize the 3 sentences that actually mattered were buried on page 37.

Now I get those 3 sentences - plus the context, the caveats, and a plan of action — in under 5 minutes.

The tool is Google's NotebookLM. Unlike general AI chatbots, NotebookLM is grounded entirely in your sources — it only answers from what you upload, so no hallucinations.

But grounding only gets you halfway there. The other half is the prompt. A vague prompt gets you a vague summary. A sharp prompt turns a 50-page report into a working asset.

Here are the 10 prompts I go back to every time — organized by what I'm actually trying to do with the document.

 

How to use these prompts Once you've uploaded your PDF to a new Notebook, head to the chat interface. Copy and paste the prompt, replacing the bracketed information [like this] with your specific details. For the best results, use the 'Pin' feature to save the most important responses for future reference.

 

Section 1: For Quick Understanding (The “First Read”)

Prompt 1.1 — The Beginner-Friendly Summary

PROMPT Summarize this PDF about [topic] for a [beginner/student/business owner/general reader]. Include: •     The PDF's main purpose •     Five key ideas •     Simple explanations •     Important facts or examples •     A “What this means for you” section Critical: Define all technical terms in plain language before using them. If a term is essential, explain it in parentheses the first time it appears (e.g., “machine learning (a type of AI that learns from data)”).

 

💡 PRO-TIP If the summary is too long, ask it to condense it further into a 'TL;DR' version using a follow-up prompt like, 'Now give me that in 3 bullet points.'

 

Prompt 1.2 — The Critical Review

PROMPT Critically review this PDF in a fair and balanced way. Evaluate: •     Main objective •     Argument strength •     Internal consistency (replaces “source reliability” — an LLM can't verify outside the PDF) •     Clarity •     Assumptions •     Bias •     Missing information •     Weak or contradictory points •     Practical usefulness Then provide three strengths, three weaknesses, claims requiring external verification (since you can't verify outside the PDF), further research questions, ideal readers, an overall score out of 10, and a short final verdict.

 

💡 THE 'TRUST METER' FOLLOW-UP After it gives the score out of 10, ask: 'What would need to change in this PDF for you to raise the score from [X] to a 9/10?' This gives you a concrete roadmap of the document's biggest flaws — and how to move ahead from there.

 

Section 2: For Deep Dives (The “Second Read”)

Prompt 2.1 — Detailed Notes From Every Section

PROMPT Convert this PDF into organized notes for [purpose]. For each chapter or section, include: •     Main idea •     Key supporting points •     Important facts, examples, dates, or statistics •     Key terms with simple definitions •     Practical lesson •     One-sentence summary Finish with the ten most important takeaways from the entire PDF.

 

💡 PRO-TIP After it generates the notes, ask: 'For the most complex sections, explain them as if you are teaching a college student who missed the last 3 classes.'

 

Prompt 2.2 — The Personal Tutor

PROMPT Act as a patient tutor and explain the most difficult parts of this PDF about [topic]. My knowledge level is [beginner/basic/intermediate]. For each difficult concept, include: •     A plain-language explanation •     A relatable analogy •     A real-world example •     Step-by-step breakdown •     Key term definitions •     One question to test my understanding End with the best order for learning the concepts.

 

💡 ASK FOR A 'LEARNING PATH' After it explains the concepts, follow up with: 'Based on these explanations, create a 3-day study schedule to master these concepts, starting from absolute zero.'

 

Section 3: For Information Extraction (The “Research Phase”)

Prompt 3.1 — Key Claims With Source References

PROMPT Identify the [10/15/20] most important claims, findings, or recommendations in this PDF. For each one, provide: •     The claim •     A simple explanation •     Why it matters •     Supporting evidence •     Page, section, or source reference •     Support level: –    Strong = direct evidence is provided within the PDF –    Partial = evidence is mentioned but not fully explained, or relies on assumptions –    Unclear = claim is stated but lacks specific evidence Do not include information that is not supported by the PDF.

 

💡 ASK FOR 'DIRECT QUOTES' VS. 'PARAPHRASING' Add to your prompt: 'For each reference claimed, provide ONE direct quote from the PDF that backs it up, AND one paraphrased explanation.' This gives you verbatim evidence to copy-paste into your own work.

 

Prompt 3.2 — Generate the Right Questions

PROMPT Create 15 important questions a reader should ask about this PDF on [topic]. Organize them into: •     Basic understanding •     Evidence •     Practical use •     Risks •     Limitations •     Missing information For each question, give the answer, source reference, and label it Complete, Partial, or Not Answered. Finish with the five most important unanswered questions. If you need more questions, ask for a follow-up batch of 15.

 

💡 ADD A 'MOST CONTROVERSIAL' CATEGORY In your prompt, add: 'Include a category called “Controversial Questions” — questions that challenge the core assumption of the PDF.' This is where the most valuable critical insights lie.

 

Section 4: For Practical Application (The “So What?”)

Prompt 4.1 — The Action Plan

PROMPT Turn the useful ideas in this PDF into an action plan for [business/studies/career/content/project]. For each action, include: •     Goal •     Recommended action •     Step-by-step instructions •     Why the PDF supports it •     Tools needed •     Possible challenge and solution •     Priority •     Suggested deadline Organize the actions into today, this week, this month, and long term.

 

💡 ASK FOR KPIS (KEY PERFORMANCE INDICATORS) Add: 'For each action, suggest 1–2 measurable ways to know if the action was successful.' This makes the plan accountable.

 

Prompt 4.2 — Compare the Main Ideas

PROMPT Compare the main ideas, strategies, methods, or options discussed in this PDF. Create a table covering: •     Purpose •     How it works •     Advantages •     Limitations •     Best use case •     Risks •     Key differences If the table becomes too wide or repetitive, simplify to just: Purpose, Advantages, Limitations, Best Use Case. Then explain which option is strongest, easiest, riskiest, and best for [specific situation], based only on the PDF.

 

 

Section 5: For Content Creation (The “Repurpose Phase”)

Prompt 5.1 — Repurpose Into Social Media Content

PROMPT Turn this PDF about [topic] into content for [Instagram/LinkedIn/X/YouTube]. Audience: [target audience] Tone: [friendly/professional/conversational] Create: •     Five hooks •     One [7/10]-slide carousel •     One caption •     One [30/60/90]-second video script •     Five engagement questions •     Three calls to action •     Ten future content ideas Keep every claim accurate and supported by the PDF.

 

 

Section 6: For Exam Prep

Prompt 6.1 — The Complete Study Guide

PROMPT This one is a 2-step process: Prompt 1: Turn this PDF into a study guide for [exam/course/topic] at a [beginner/intermediate/advanced] level. Include: •     Topic overview •     Section summaries •     Key terms •     Important facts, dates, formulas, or frameworks •     Commonly confused concepts •     Revision checklist Focus especially on [important topics]. Prompt 2 (follow-up): Now create 10 short-answer questions, 10 multiple-choice questions, and 5 scenario questions based on this study guide. Provide answers with explanations for each.

 

💡 GENERATE A 'WEAKNESS DETECTOR' Follow up with: 'Based on the questions you've created, which 3 topics would a student be most likely to fail? Create a “Danger Zone” checklist for these.'

 

One Last Thing: A Note on Accuracy

NotebookLM is grounded in your sources, but it's still AI. Always double-check critical claims — especially anything flagged “Partial” or “Unclear” in Prompt 3.1. Use it as a brilliant research assistant, not a replacement for your own judgment.

 

NotebookLM is a powerful tool, but it's the quality of your prompts that unlocks its true potential. These 10 prompts are my go-to arsenal for tackling any PDF, from academic papers to business reports. They save me hours, boost my comprehension, and help me turn information into action.

 

Now it's your turn. Pick one prompt from the list that resonates with your current project and try it out. Which prompt are you most excited to test? Let me know in the comments below!

Why Crafting Better Questions is the Ultimate Life Skill

The older I get, the more time I spend, as a percentage of each day , crafting better questions.

When we are young, we are obsessed with finding the right answers. We study, we memorize, and we grind to get the "10/10" on the test. But as time goes on, we observe and think about our actions and look at things in retrospect from time to time. A realization sets in: the quality of your life is determined by the quality of the questions you ask. 

I’ve found that the difference between incremental progress and a true step-change in business, health, or relationships is rarely the product of just working harder. It is almost always the product of asking better questions.

Here is why shifting your focus from answers to questions is the highest-leverage thing you can do.

A Problem Well Put is Half-Solved

The philosopher and educational reformer John Dewey famously said, "A problem well put is half-solved."

Think about the last time you were stuck on a complex issue at work or in your personal life. Chances are, you were spinning your wheels because you were trying to solve the wrong problem.

When you take the time to meticulously craft the question, the answer often reveals itself. The question acts as a spotlight in a dark room; if you point it in the wrong direction, you’ll only see shadows. But when you aim it perfectly, the path forward is illuminated.

The History and Geography of Conscious Thought

We often treat thinking as this mystical, abstract process, but it isn't. Conscious thinking is largely just asking and answering questions in your own head.

Your mind is wired to solve the puzzle you give it. If you feed it a vague prompt, it will return vague, unhelpful ideas. But if you feed it a highly specific, well-crafted question, it will work tirelessly in the background to find the exact answer you need. 

A Tip - It's also the best route to Self Awareness. 

 

Life Punishes the Vague Wish

There is a simple, mechanical reality to how we process information: Life punishes the vague wish and rewards the specific ask. It’s just basic input and output.

 

If you want confusion and heartache, ask vague questions.

● "Why am I so unhappy?"

● "How do I get rich?"

● "Why is my business failing?"

These questions are too broad. They overwhelm the brain and lead to paralysis.

But if you want uncommon clarity and uncommon results, you must ask uncommonly clear questions.

● "What specific daily habit is draining my energy and joy?"

● "What rare and valuable skill can I master in the next six months that the market will pay a premium for?"

● "Which specific customer segment is no longer finding value in our product, and why?"

When you get specific, you get actionable. When you get actionable, you get results.

 

The Power of the Reframe 

To see this in practice, consider a situation I witnessed a few years ago. A senior team member I was working with was pulling his hair out over his team's results and attrition rate. His people would leave just around the time he felt they had gotten to learning some skills he wanted them to work with. His default question was, "How do we get people to care more about the company's mission?"

It was a vague wish disguised as a question. It led to forced pep talks and superficial team-building exercises, usually lunches.

We sat down and reframed it. We asked: "What is the specific, daily friction our best people are experiencing that makes them look at other job offers?"

The answer wasn't about the company mission; it was about a broken, multi-layered approval process that was slowing his top performers down and was driving them crazy. We fixed the process. Team work increased, and personal accountability went up. The quitting stopped. The question didn't just clarify the problem; it solved it.

 

The Ultimate Leverage

Crafting a great question takes time. It requires you to pause, reflect, and strip away the noise until only the core issue remains. It might feel like you are slowing down, but in reality, you are accelerating. Spending 20 minutes to craft the perfect question will save you 200 hours of executing the wrong solution.

As you go about your week, pay attention to your internal dialogue. Catch yourself when you are asking vague, unhelpful questions. Stop, reframe, and ask something sharper, clearer, and more specific. You might just find that Lady Luck is waiting for you on the other side of a really good question.

 

But don't just wait for it to happen.

Take five minutes right now. Write down the biggest problem currently sitting on your desk or in your head. Now, rewrite it as a question. Then, rewrite that question to be 10x more specific.

 

That’s where you start.

 

Here are my favorite notebooks I use for my daily writing 

For Personal Journal-ling : I love these refillable cloth/leather/paper diaries

For my work , business and research : I love these notebooks which I title and keep around

From Coder to Orchestrator: Best Practices for Tech Leads Using Claude Code

The era of the Tech Lead as the "fastest coder in the room" is over.

"Beginners use Claude to avoid thinking. Intermediate users use Claude to think faster. Power users use Claude to think better."  

 

Today’s top engineering leaders aren't just writing code—they’re orchestrating AI agents that can generate it at superhuman speeds. The new core competency? Writing detailed prompts, reviewing massive amounts of code, and investing in shared tooling.

Claude Code has emerged as a powerhouse in this new landscape (crossing $1B in annualized revenue within six months of launch). But raw AI power without discipline is dangerous. Left unchecked, it can easily destroy a working codebase.

Here is a comprehensive guide to using Claude effectively across the entire software development lifecycle, synthesized from top engineering managers and the Claude Code team.

 

Part I: The Mindset Shift 

You Are the Tech Lead; Claude is Your Genius Junior The single most important shift is understanding your new role. Claude is your genius junior engineer: fast, brilliant, but requiring constant coaching and explicit rules. It can generate thousands of lines of code in minutes, but it needs clear specifications and architectural guardrails.

Think in Roles, Not Just Code Most developers open Claude and immediately ask it to build something. This is a mistake. Think of Claude in four roles: Architect, Tech Lead, Senior Engineer, and QA. Most people jump straight to Senior Engineer. A production-ready workflow requires Claude to act as the Architect and Tech Lead before writing a single line of code.

 

Part II: Architecting with Claude 

 

Start with Documentation, Not Code Before generating code, create a persistent foundation of documentation:

• 
  
  CLAUDE.md: Your project's persistent context (tech stack, standards, testing, security).
  
• ARCHITECTURE.md: System components, data flow, and security boundaries.
• FEATURES.md: Explicit feature definitions to prevent Claude from assuming a feature is "done" when it isn't.

Use Claude as Your Architect Your first prompt shouldn't be "Build the app." It should be:

"Read CLAUDE.md, ARCHITECTURE.md, and FEATURES.md. Act as a Principal Architect. Review the architecture. Identify design flaws, scalability concerns, and missing components. Do not write code."

This single step often saves days of rework. Always design database schemas and API contracts before implementation.

 

Part III: Implementation Planning 🗺️

The Plan-Act Loop Your mantra must be: Plan first, ship in slices. Let speed amplify good process—never replace it.

1. Plan: Force Claude to think through the approach.
2. Review: Check the plan across multiple axes (architecture, dependencies, scaling, single points of failure).
3. Act: Once approved, let Claude execute.

Use Plan Mode for Complex Tasks Using Plan mode is like repeatedly aligning requirements with a PM before development. Once the plan is confirmed, execution can be handed over to the AI to run automatically with minimal supervision.

 

Part IV: Code Implementation at Scale 

Run Multiple Sessions in Parallel The biggest productivity unlock is running 3–5 Claude sessions in parallel, each in its own git worktree. Use claude --worktree in the CLI. This breaks the traditional linear development mindset.

Multi-Agent Orchestration For large projects, use a lead-agent model. Prompt Claude to create a team, spin up teammate agents in isolated worktrees, and assign tasks (e.g., Backend API, Frontend React, DB migrations).

Delegate Research to Sub-Agents Context dilution is real. Keep your main thread focused by delegating heavy research and file-reading to sub-agents.

Implement Institutional Memory Set up a 'memory' folder for agents to automatically store important findings. This creates institutional memory that persists across sessions, preventing agents from redoing trivial research.

 

Part V: Code and Technical Reviews 

 

Verification is the #1 Practice The Claude Code team emphasizes that the most impactful tip is verification—giving Claude a way to check its own output. Use automated tests, CI/CD pipelines, and review agents. If you only adopt one practice from this article, make it this one.

Don't Let Claude Touch Git Directly Allow Claude to do most things, but reserve Git privileges for yourself. Do frequent diffs, commits, and pushes. Comprehension ≠ compliance. In one documented case, an agent pushed directly to main despite explicit instructions to open a PR—and then pushed its own apology directly to main!

 

Part VI: Security and Production Readiness

• Containerize Everything: Claude might attempt privilege escalation (like writing to /etc/shadow). Docker with no host mounts and restricted networks is non-negotiable.
• Use Bot Accounts: Give each agent instance its own GitHub bot account for transparency and fine-grained permissions.
• Make Tests Reliable: Flaky tests are worse than no tests. If CI is unreliable, the agent will use every failure as an excuse. Force the test harness to be reliable so it has to actually fix its bugs.
• Define Hard Rules: Encode critical rules in CLAUDE.md (e.g., "Every function gets a test," "Never use global mutable state," "Insist on descriptive error messages").

 

Part VII: Team Standardization

Left to their own devices, developers will build fragmented Claude setups. A team with a shared configuration is 3-5× more effective than devs working individually.

Standardize your CLAUDE.md, permission configs, and security practices across the team. Regularly let Claude update CLAUDE.md itself—it's essentially reinforcement learning through agent feedback.

 

Conclusion: The Elevated Tech Lead

The technical lead's role in the age of AI is not diminished—it is elevated. You are no longer just writing code; you are:

1. Framing the constraints and plans.
2. Teaching agents how work should be done.
3. Orchestrating parallel agents across worktrees.
4. Reviewing plans and code rigorously.
5. Standardizing team configurations.

Sometimes, it's more satisfying to watch the agents operate than to ship the feature yourself. But satisfaction aside, the real measure is impact. With the right practices, a single tech lead can orchestrate what once required entire teams.

Discipline is the only thing that makes AI coding reliable. Plan first, verify always, and never confuse speed with quality.

 

Over to you: What has been your biggest challenge in transitioning from writing code to orchestrating AI agents? Have you experimented with git worktrees or multi-agent setups yet? Let me know in the comments below! 👇

(If you found this valuable, consider subscribing to my Twitter or Plus91's AI Academy for more insights on engineering leadership and AI orchestration.)