How to read for deep comprehension with LLMs

Copy the text and paste it into your LLM. Ask specific questions to translate abstract concepts into actionable insights. You build deep expertise and retain important knowledge.

I read a lot of content every single day. I read articles during the day and novels at night. I don’t even own a television. I honestly cannot remember the last time I watched a series. I’m not saying this to brag or sound like an intellectual. I only mention it to make one specific point. I spend a huge chunk of my life consuming text. And recently, LLMs completely changed how I read.

Avoid the quick summary trap

I’m not talking about using AI for aggregation or synthesis. Many of my friends are building custom tools to aggregate content. They scrape the daily AI news. They use AI to summarize everything into a quick podcast. They think this is the perfect time-saving solution. But what is the point of saving ten minutes on a good article just to spend that extra time doomscrolling? I look at these summary tools today and I see something incredibly superficial.

The French philosopher René Descartes said:

The reading of all good books is like a conversation with the finest minds of past centuries.

A literal conversation with the finest minds of past centuries

I completely agree. Every piece of text is literally a conversation with a real person. And it’s the best return on investment you can find. You pay twenty euros for a book: you get someone’s entire life of work. You spend ten minutes on an article: you absorb hours of their deep thinking. It’s an amazing deal. You just have to make the effort.

The problem is that this conversation only goes in one direction. The writer always has a specific audience in mind. Often, that target audience is not you. Think about all the new articles about AI. The topics are fascinating and highly relevant to our product work. However, developers usually write these articles for other developers. They use highly technical jargon. If you don’t know how to code, you feel completely lost. You miss out on valuable product insights because the text is simply too dense.

I talked about technical articles. But this isn’t just about developers writing for developers. You face the same issue with other topics. It happens with economics, sociology, philosophy, etc. Writers always target a specific audience. If you only read content written specifically for you, you fall into a dangerous trap. You lock yourself inside an echo chamber. You only consume what others have already digested and simplified. You stop thinking outside your bubble. This limits your innovation. You need a way to truly understand complex topics without relying on shallow summaries.

Turn reading into a dialogue

This is exactly where LLMs come into play. They make reading feel like magic. I use the word magic intentionally here. I’m a fan of the Harry Potter books. In the second book, there is a very specific magical object. It’s a completely blank diary. The pages activate only when you ask a question. The book reads your words. It writes back to you. It creates a real-time dialogue. I read that as a child. It became my ultimate dream.

Tom Riddle's diary: the ultimate two-way reading experience

I always wanted that exact interaction with my books. I wanted the true conversation Descartes mentioned. Turning a complex text into a friendly chat fixes the one-way problem.

Let me walk you through my exact method using a very concrete example.

Paste the full article into AI

Recently, I found an article claiming that MCPs are dead. Eric Holmes, the author, argued that Command Line Interfaces, or CLIs, are the future. This was bad news for me. I had just installed about ten MCPs on Cursor. I really needed to understand this shift. The problem was the article targeted engineers. I didn’t even know what a CLI actually was at the time. I stared at my screen like an illiterate. The content was clearly important. I just didn’t know how to apply it to my own work.

This is where the magic happens. You are no longer alone when you read. I use a technique I call the savage Ctrl+A.

The savage Ctrl+A technique in four simple steps

1. I press Ctrl+A on my keyboard. I select the entire article on the page. I grab all the text. I even include the messy website menus and the footer. It really doesn’t matter.

2. I copy everything with Ctrl+C. Everything.

3. Then, I open my favorite LLM. Right now, I use Gemini.

4. I write a very simple prompt. I type, “Explain this to me. I am a beginner and a non-technical person.” I type three dashes to separate my instructions. Finally, I paste the entire web page right into the prompt box.

Here is the action in slow motion:

The source material: dense, technical, and full of jargon

Prompt:

Explain this to me. I am a beginner and a non-technical person.
---
[Raw text copied from the website…]

Translating developer jargon into plain English

Result: The AI instantly acted as a perfect translator. It stripped away the developer ego jargon. It gave me the big picture. It told me the author thinks the tech industry got distracted by a shiny new toy (MCP) when an old, reliable tool (CLI) does the job better. It gave me five simple, business-focused reasons why. I went from completely lost to understanding the core debate in thirty seconds.

Now that the article is inside the context window, I can start the conversation. I talk directly with the text. I always start with a basic request:

Explain what a CLI is. Use simple terms.

Breaking down complex concepts with everyday comparisons

Result: The AI stripped away the jargon by comparing a CLI to how I normally use my computer. It contrasted the visual buttons and mouse clicks we use every day with the blank, text-only screen of a command line. It made the underlying concept instantly relatable without relying on a single line of code.

Then, I ask about my specific situation. I want to know how this impacts my daily work:

I currently use the Atlassian MCP to manage my Jira tickets. Based on this article, why exactly would a CLI approach be better or worse for my workflow?

Making the text directly relevant to my daily workflow

Result: The AI mapped the author’s highly technical arguments directly to my daily work. It broke down exactly how a CLI would mean fewer annoying Jira log-ins, fewer random freezes, and safer permissions for my specific tickets. It transformed abstract engineering theory into a concrete assessment of my own tools.

I am a visual learner. I need to see things to understand the system:

Create a simple text-based diagram. Show the data flow of an MCP setup on the left. Show the data flow of a CLI setup on the right. Highlight the differences.

Visualizing abstract systems with text-based diagrams

Result: The AI generated a clean, comparative breakdown of the two architectures. It showed me the messy, extra layers of an MCP versus the direct path of a CLI. Seeing it visually mapped out made the abstract engineering concepts instantly click in my brain.

I need a non-technical metaphor to explain this to my stakeholders:

Explain the difference between MCP and CLI using a simple analogy.

The restaurant analogy: paper tickets versus pneumatic tubes

Result: The AI gave me a restaurant metaphor. The CLI is a standard paper order ticket used by human waiters. The MCP is a highly expensive, glitchy pneumatic tube system built specifically for a robot to use. This kind of analogy is helpful when you have to explain technical debt to business stakeholders.

I need an action plan to start the transition with my colleagues:

Write a step-by-step practical guide. How do I transition a product manager team from using MCPs to using CLIs next week?

Here, I need to edit and iterate on the guide. So I simply activate Gemini’s Canvas mode. This lets me modify the documents right there on the screen. I can then export it directly to Google Docs with one click.

Generating a concrete action plan using the Canvas tool

Result: The AI gave me a highly detailed, 4-phase rollout plan. But it also acted as a guardrail. It gave me a strict reality check first, warning me that I would absolutely need an engineer to help run the installation scripts. It gave me the exact game plan, but kept my expectations grounded in reality.

I need to anticipate what will break if we make this change:

Act as a pessimistic engineer. Tell me the three biggest risks of abandoning MCPs for CLIs right now.

Playing devil's advocate to expose hidden risks

Result: The AI played devil’s advocate perfectly. It warned me about massive security risks (giving an AI the keys to your entire computer) and the nightmare of IT support for non-technical users. It red-teamed the original article. It exposed the author’s blind spots. This is how you build real risk management into your reading.

I want to know what the other experts are doing. I explicitly write in my prompt to search the internet:

Search the web for recent trends. Are the main AI experts adopting CLIs over MCPs? Summarize the expert consensus.

Grounding opinions with real-time web search and market data

Result: The article was just one man’s opinion. The AI searched the live web and brought me the actual market reality. It turns out the headline was clickbait. Solo developers use CLIs to save money, but major enterprises mandate MCPs for strict security and governance. The AI grounded the author’s opinion in objective, current market data.

I need to present this strategy to the executive board:

Generate a five-slide presentation for top management based on this article.

From reading an article to a pitch-ready slide deck

Result: The AI generated an actual, fully formatted visual presentation right there in the chat. It built the slides, organized the bullet points into clean columns, and gave me a button to instantly export it to Google Slides. With some effort, I went from not understanding a highly technical article to having a ready-to-pitch visual deck for my CEO.

These prompts are just examples. You can interact with the text in many other ways. Yes, I know I’m not actually talking directly to the real author. But it certainly gives that exact impression. This entire process completely changes how I learn. I prefer to spend my time dissecting one single article like this. It’s far better than passively skimming a hundred headlines.

You are probably thinking about NotebookLM right now. You can absolutely use it for this. NotebookLM only reads the documents you upload. In many cases, this strict boundary is actually its star feature. It guarantees the AI only uses your specific text. It’s perfect for an isolated PDF or generating an audio podcast. But this closed system becomes annoying when you need to tap into the LLM’s vast memory or connect a new article to outside industry trends. You just have to choose the right tool for the job.

Talk to your next article

I just showed you my process. Now I want you to try it yourself. I have the perfect article in mind for this exercise. It’s a recent case study from DoorDash and Deliveroo. They explain how their research teams deploy AI agents to analyze user data. They bypass traditional engineering bottlenecks completely. They even built a custom multi-agent system to process raw customer interviews. I really love their specific use cases and their clear explanations. I want you to open this article on your screen. Give it a very quick skim to understand the context.

Then I want you to use the savage copy and paste method I just shared. Grab all the text on the page. Drop it into your favorite LLM context window. Start a real dialogue with the text. Keep talking with the LLM until you generate a concrete workflow for your own enterprise. You can pull the thread all the way to the end. You can even generate the specific agent instructions needed to replicate their results. You will quickly see how much value you get from a single conversation. You will turn a simple blog post into an actionable blueprint for your next AI-powered workflow.

Learn actively with AI

I see many headlines claiming AI makes us lazy. I just read an Anthropic study about this exact topic. The researchers tested how developers learn a new programming library with AI. The results are a huge warning for product builders. Participants who simply delegated the work to AI learned almost nothing. Their conceptual understanding dropped. Their debugging skills actually degraded. They didn’t even finish the tasks significantly faster. This happens when you use AI as a dumb shortcut.

But the researchers found a massive exception. They identified a few specific high-scoring interaction patterns. Some users asked pure conceptual questions. Others asked the AI to explain the output it just generated. These specific users stayed cognitively engaged. They preserved their learning outcomes completely. They mastered the new skills while still using the AI tools. This perfectly validates the two-way reading method. You don’t get dumber when you actively interrogate the text.

You must choose your interaction pattern carefully. You can read passive summaries. You can delegate your core thinking to AI. But that is a trap. Instead, you can choose to have active conversations with your reading material. You can force your brain to engage. You can use the AI to dig deeper instead of skimming the surface. This is how you actually build expertise.

Grab your next complex article, paste it into your LLM and start your first real dialogue!