Friday, February 6, 2026

6 smart prompts that make NotebookLM way more useful

 Article

A few months back, Google added a feature called Slide Deck to NotebookLM, which lets you convert sources uploaded to a notebook into a full-fledged presentation.

This feature is powered by Google's Nano Banana image model, and you can currently create two Slide Deck formats: Detailed Deck or Presenter Slides. The Detailed Deck is perfect for generating a comprehensive presentation with maximum information from your sources, while Presenter Slides are optimized for on-screen clarity, focusing on concise bullet points and visuals that make your talk easy to follow.

In addition to selecting the format and language of the Slide Deck, you can also choose between Short or Default deck lengths. However, if you hit the Generate button after configuring these settings, the deck produced might feel very generic.

While it will be customized to your sources and include only relevant visuals that reflect the content in your notebook, you can really only use it as a starting point. And since NotebookLM doesn’t let you edit Slide Decks just yet, the output can’t serve as more than a rough draft or baseline.

That said, I’ve found that a detailed prompt works wonders with the Slide Deck feature. By explicitly specifying everything, from the presentation title and what each slide should include to the overall design scheme and visual style, you can push NotebookLM to generate a deck that feels far more intentional and presentation-ready right out of the gate.

Here's a prompt you can tweak that I rely on extensively:I have attached a script for a [X-minute] presentation delivered to [audience type] titled "[Presentation Title]."

Please generate a Slide-by-Slide Outline for this script. For each slide, provide:

Slide Title: Catchy and relevant to the target audience.

Visual Description: Specific instructions on what image, diagram, or screenshot should appear on the slide. (Include placeholders for any interactive elements, like QR codes, if needed.)

On-Screen Text: Maximum 3–5 bullet points. Keep it minimal. Please not paste the whole script. Use keywords or technical terms relevant to the topic.

Design Style: [e.g., Dark mode, minimalist, professional, playful, futuristic — choose a style that fits your audience].

Optional Guidance on Key Sections: Provide your own headings if you like, but consider including:

  • Introduction / Hook
  • Problem / Challenge
  • Solution / Approach
  • Interactive Moment / Example
  • Core Concept / Key Terms
  • Conclusion / Takeaways

The "five essential questions" prompt

Stop using NotebookLM for just answers

notebooklm five questions prompt
Remove Ads

1.) Analyze all inputs and generate 5 essential questions that, when answered, capture the main points and core meaning of all inputs.

2.) When formulating your questions:

a. Address the central theme (or themes if there are many) or argument (or arguments if many).

b. Identify key supporting ideas

c. Highlight important facts or evidence

d. Reveal the author's purpose or perspective

e. Explore any significant implications or conclusions.

3.) Answer all of your generated questions one-by-one in detail.

Audio Overview formats

A mixed bag

NotebookLM app open on a Honor Pad 8Credit: Tashreef Shareef / MakeUseOf

NotebookLM’s viral Audio Overview feature, which lets you convert your sources into engaging podcasts, is one of my favorite ways to passively learn. I generate a podcast, plug in my AirPods, and learn on the go. With Audio Overviews, I’ve noticed that the more you generate and listen to them, the more monotonous they can become.Even if they’re about wildly different topics, you’ll begin noticing patterns in the way the hosts speak, explain ideas, and transition between prompts. That’s why customizing them with prompts makes such a big difference. Here are some of my favorite Audio Overview prompts:

The “Teach It to a 12-Year-Old” prompt

Have you heard about the Feynman Technique? Chances are, you know about the technique — you just might not know it by name. At its core, the Feynman Technique is about learning by teaching: if you can explain a concept simply and clearly, you actually understand it. And if you can’t, that’s usually a sign there are gaps in your knowledge. Here’s a prompt that applies this idea directly to NotebookLM’s Audio Overviews:

"Explain the core concepts in this source as if you are teaching a smart 12-year-old. Use analogies for every complex term. If there is a concept that is abstract, ground it in a real-world physical example. Focus on why this matters, not just what it is."

Quick meeting brief

Meeting notes have fundamentally changed since AI entered the game, and NotebookLM’s Audio Overviews feature is an excellent way to passively get a recap of key points. However, a conversational, podcast-like discussion isn’t always the most efficient way to absorb information when you’re pressed for time. That’s where the prompt below comes in handy:"You are briefing a busy CEO. Do not use fluff or banter. Go straight to the bottom line: What is the problem, what is the solution, and what are the financial implications? If there are action items in the text, list them clearly. Keep the tone professional, crisp, and urgent."

The “Chaotic Podcast Hosts” Prompt

Sometimes, you just want to listen to a really, really chaotic podcast. Why not convert your sources into a wild, improvisational discussion using NotebookLM? With this prompt I found on Reddit (and slightly polished myself), you can generate scenarios where hosts clash, trade witty insults, and riff off improvised backstories. It's an incredibly fun way to listen to your sources in a completely different yet entertaining format.

Host names: Amber & Rick.

Hosts dislike each other and the topics they’re discussing.

Dialogue style: Dry, sly, witty, and hilariously underhanded insults. Incorporate sarcasm, thinly veiled cynicism, and unrestrained contempt.

Storytelling: Improvise implied backstory, history, relationships, and personal knowledge to create fodder for embarrassment or humiliation. Include a few underlying story arcs that gradually unfold.

Escalation: Have the hosts go at each other increasingly viciously until they lose their temper and yell venomously.

Resolution: End with one host making a completely unexpected, hilarious joke that breaks the tension and makes both laugh uncontrollably.

There are endless possibilities

The six prompts I’ve covered above are just the tip of the iceberg. Practically every NotebookLM feature is super customizable — it’s totally up to you how descriptive you want to be with your prompts!

Google Ads no longer runs on keywords. It runs on intent.

 

If you’re still building Google Ads around keywords, you’re behind. Here's what that means for eligibility, structure, and PPC strategy.

Most PPC teams still build campaigns the same way: pull a keyword list, set match types, and organize ad groups around search terms. It’s muscle memory.

But Google’s auction no longer works that way.

Search now behaves more like a conversation than a lookup. In AI Mode, users ask follow-up questions and refine what they’re trying to solve. AI Overviews reason through an answer first, then determine which ads support that answer.

In Google Ads, the auction isn’t triggered by a keyword anymore – it’s triggered by inferred intent.

If you’re still structuring campaigns around exact and phrase match, you’re planning for a system that no longer exists. The new foundation is intent: not the words people type, but the goals behind them.

An intent-first approach gives you a more durable way to design campaigns, creative, and measurement as Google introduces new AI-driven formats.

Keywords aren’t dead, but they’re no longer the blueprint.

The mechanics under the hood have changed

Here’s what’s actually happening when someone searches now.

Google’s AI uses a technique called “query fan out,” splitting a complex question into subtopics and running multiple concurrent searches to build a comprehensive response.

The auction happens before the user even finishes typing.

And crucially, the AI infers commercial intent from purely informational queries.

For instance, someone asks, “Why is my pool green?” They’re not shopping. They’re troubleshooting.

But Google’s reasoning layer detects a problem that products can solve and serves ads for pool-cleaning supplies alongside the explanation. While the user didn’t search for a product, the AI knew they would need one.

This auction logic is fundamentally different from what we’re accustomed to. It’s not matching your keyword to the query. It’s matching your offering to the user’s inferred need state, based on conversational context. 

If your campaign structure still assumes people search in isolated, transactional moments, you’re missing the journey entirely.

Anatomy of a Google AI search query

Dig deeper: How to build a modern Google Ads targeting strategy like a pro

What ‘intent-first’ actually means

An intent-first strategy doesn’t mean you stop doing keyword research. It means you stop treating keywords as the organizing principle.

Instead, you map campaigns to the why behind the search.

  • What problem is the user trying to solve?
  • What stage of decision-making are they in?
  • What job are they hiring your product to do?

The same intent can surface through dozens of different queries, and the same query can reflect multiple intents depending on context.

“Best CRM” could mean either “I need feature comparisons” or “I’m ready to buy and want validation.” Google’s AI now reads that difference, and your campaign structure should, too.

This is more of a mental model shift than a tactical one.

You’re still building keyword lists, but you’re grouping them by intent state rather than match type.

You’re still writing ad copy, but you’re speaking to user goals instead of echoing search terms back at them.

Get the newsletter search marketers rely on.


What changes in practice

Once campaigns are organized around intent instead of keywords, the downstream implications show up quickly – in eligibility, landing pages, and how the system learns.

Campaign eligibility

If you want to show up inside AI Overviews or AI Mode, you need broad match keywords, Performance Max, or the newer AI Max for Search campaigns.

Exact and phrase match still work for brand defense and high-visibility placements above the AI summaries, but they won’t get you into the conversational layer where exploration happens.

Landing page evolution

It’s not enough to list product features anymore. If your page explains why and how someone should use your product (not just what it is), you’re more likely to win the auction.

Google’s reasoning layer rewards contextual alignment. If the AI built an answer about solving a problem, and your page directly addresses that problem, you’re in.

Asset volume and training data

The algorithm prioritizes rich metadata, multiple high-quality images, and optimized shopping feeds with every relevant attribute filled in.

Using Customer Match lists to feed the system first-party data teaches the AI which user segments represent the highest value.

That training affects how aggressively it bids for similar users.

Dig deeper: In Google Ads automation, everything is a signal in 2026

The gaps worth knowing about

Even as intent-first campaigns unlock new reach, there are still blind spots in reporting, budget constraints, and performance expectations you need to plan around.

No reporting segmentation

Google doesn’t provide visibility into how ads perform specifically in AI Mode versus traditional search.

You’re monitoring overall cost-per-conversion and hoping high-funnel clicks convert downstream, but you can’t isolate which placements are actually driving results.

The budget barrier

AI-powered campaigns like Performance Max and AI Max need meaningful conversion volume to scale effectively, often 30 conversions in 30 days at a minimum.

Smaller advertisers with limited budgets or longer sales cycles face what some call a “scissors gap,” in which they lack the data needed to train algorithms and compete in automated auctions.

Funnel position matters

AI Mode attracts exploratory, high-funnel behavior. Conversion rates won’t match bottom-of-the-funnel branded searches. That’s expected if you’re planning for it.

It becomes a problem when you’re chasing immediate ROAS without adjusting how you define success for these placements.

Dig deeper: Outsmarting Google Ads: Insider strategies to navigate changes like a pro

Where to start

You don’t need to rebuild everything overnight.

Pick one campaign where you suspect intent is more complex than the keywords suggest. Map it to user goal states instead of search term buckets.

Test broad match in a limited way. Rewrite one landing page to answer the “why” instead of just listing specs.

The shift to intent-first is not a tactic – it’s a lens. And it’s the most durable way to plan as Google keeps introducing new AI-driven formats.

Wednesday, February 4, 2026

I didn’t know NotebookLM could do this — 10 features hiding in plain sight

 

I didn’t know NotebookLM could do this — 10 features hiding in plain sight

NotebookLM logo
I’ll admit it: I thought NotebookLM was just a glorified summarizer or study helper for students. I figured it was nothing more than "Upload a PDF, ask for a TL;DR and move on."

Frankly, that’s how I used it for months. But after spending more time actually exploring the menus, toggles and prompts, I realized Google’s AI research assistant can do far more than condense documents. Some of its most useful tools are sitting right in the interface — easy to overlook unless you go looking.

From turning your notes into a podcast to pulling quotes with citations and even browsing live web sources, NotebookLM has subtly evolved into something closer to a personal research analyst than a simple note-taker.

Here are 10 features I genuinely didn’t realize were there — and most users probably miss.

1. It now pulls in live web sources

NotebookLM

(Image credit: Future)

Not sourcing the internet used to be NotebookLM’s biggest limitation. Originally, it only worked with files you uploaded. Now, you can add web sources and pull in current articles alongside your documents. This completely shifts NotebookLM from a closed study tool into a live research assistant.

That means you can:

  • Compare fresh news to older PDFs
  • Update research without re-uploading files
  • Expand context beyond static sources

2. Cross-document comparison is built in

NotebookLM screenshot

(Image credit: Future)

If you're working on understanding how two subjects relate or differ you can avoid manually doing it yourself. NotebookLM doesn’t just summarize individually — it synthesizes across sources, which is incredibly useful for research briefs and comparative analysis.

Simply upload multiple PDFs or links and ask:

  • “Where do these disagree?”
  • “What themes overlap?”
  • “What trends appear in all reports?”

3. It can turn your documents into a podcast

Google Audio Overview feature from NotebookLM

(Image credit: Google)

NotebookLM’s Audio Overview feature generates a conversational, podcast-style summary of your uploaded sources. Two AI voices discuss your material like co-hosts, highlighting key themes and disagreements.

It’s surprisingly natural and incredibly useful if you prefer listening over reading — or want to “review” notes while walking or driving. You can even "call in" and ask your questions the same way you would with a real podcast. From there, the AI voices address your questions and answer them in real-time.

4. Every answer comes with citations

Person typing on a laptop in a low lit room

(Image credit: Olena Malik / Getty Images)

Unlike many chatbots, NotebookLM shows exactly where information comes from inside your sources. You can click highlighted passages and jump straight to the original text.

For journalists, students or anyone double-checking accuracy, this alone makes it stand out. Once you've seen the source, you can branch out and explore it or scratch it completely. Either way, you'll never have to second guess accuracy because you can fact-check the citations for yourself.

5. It generates instant study guides

NotebookLM presentation

(Image credit: Future/Amanda Caswell)

If you’ve ever tried to manually turn a 40-page PDF into study material, this feature is a game-changer. Since I am not a student, I figured I'd never need this feature. However, I've used it multiple times while preparing for TV interviews and meetings.

Dense reports can become:

  • Flashcards
  • Key term lists
  • Practice questions
  • Review sheets

6. It builds timelines automatically

NotebookLM

(Image credit: Future)

Ask NotebookLM to extract chronological events and it will produce a structured timeline from interviews, articles or long-form reports. Instead of hunting for dates manually, you get a clean sequence in seconds.

This is especially helpful for:

  • Investigative research
  • Historical topics
  • Project planning
  • Legal or policy documents

7. Quote finder with context

NotebookLM

(Image credit: Future/NPowell)

One of the most underrated strengths of NotebookLM is its ability to pull the strongest quotes on a topic while also including the surrounding context, rather than surfacing isolated one-liners. You can prompt it to “find the most relevant passages” or “highlight the best supporting quotes,” and it will return excerpts along with the paragraphs before and after, so you understand why that quote matters.

This is important because many AI tools tend to strip statements out of their original setting, which can unintentionally distort meaning or exaggerate claims.

8. FAQ creation from raw documents

NotebookLM

(Image credit: Tom's Guide)

Another standout capability of NotebookLM is its ability to automatically generate a clear, well-structured FAQ section from long documents like manuals, white papers or in-depth guides.

Instead of manually skimming dozens of pages to pull out the most common questions and answers, you can simply upload the document and ask for an FAQ — the tool identifies key themes, recurring concepts and practical “what users really need to know” moments on its own.

9. “Explain it like I’m 10” mode

Full presenation from NotebookLM

(Image credit: Future/Amanda Caswell)

As a visual learner, when I was in school, I often had trouble grasping concepts the first time. I wish I had NotebookLM back then to support my learning. Because, rewriting complex topics into simpler forms is where NotebookLM shines.

For students or professionals struggling with terminology, legal clauses or research language and immediately felt overwhelmed, this feature can be a genuine sanity saver. It allows you to grasp the substance of a document faster, ask smarter follow-up questions and engage with material you might otherwise avoid.

10. Debate and counter-argument prep

Person typing on laptop in the dark

(Image credit: d3sign / Getty Images)

You can stop brainstorming in a vacuum or relying on vague AI generalizations as you prepare for a meeting. NotebookLM is also particularly effective at structuring balanced arguments for and against a topic using only the sources you provide, you can rest assured that you'll have the right talking points built directly from your uploaded material. This makes it especially valuable for presentations, essays, debate prep and editorial planning where accuracy and balance are essential.

Bottom line

If you're using a "regular" chatbot for any of the above, it's worth checking out NotebookLM. Between audio overviews, live web integration and cross-document synthesis, it’s one of the most practical AI tools for research-heavy work.

With so many powerful AI features, it's a great way to add flexibility and productivity to your workflow.