Four buckets of practical AI for individuals
AI isn't just for generation. Three practical skills worth mastering—that aren’t creating from scratch.
Most people hear “Generative AI” and take it at face value. Yes, AI spits out content—drafts blog posts, generates images, composes music, makes up statistics. But that’s an incomplete picture of what capabilities AI holds for us as users.
There’s an unsung realm of AI capabilities outside the bounds of creating from scratch. AI is equally useful with summarizing, researching, or assimilating information. And yet, these functions are often overlooked simply because of how we talk about AI as generative.
Key Takeaways
AI isn’t just about generating content—its power also lies in research, summarization, and assimilation
The most effective AI workflows start with research, then move to summarization, assimilation, and finally generation
If you’re only using AI to generate content, you’re leaving its most valuable capabilities untapped.
Think of AI like an unethical librarian. Of course you could have them write a book for you (what a library!), but they can also help you with:
Research: Digging through an archive to find the most relevant materials on a topic.
Summarization: Reading a stack of books and handing you a one-page summary of the key ideas.
Assimilation: Organizing books, news articles, and video documentaries into a structured outline, normalizing scattered formats into something homogenous.
Let’s break down each of these four categories, why they matter, and what they look like. Afterwards, we’ll dig into how jumping straight to generation with AI is akin to using AI backwards.
Generation: Creating new content
The AI function everyone knows best. Give it a theme or idea and receive something new in return. AI tools like ChatGPT, Midjourney, and Copilot excel at this. The funny thing is, because of how we talk about AI, people assume that generation is most of what it does.
Practical examples:
Drafting blog posts, social media captions, or product descriptions.
Designing concept art or refining ideas visually.
Producing boilerplate code or offering suggested completions.
What generation prompts look like
Clear deliverable: “Write a 300-word product description for a smart thermostat aimed at eco-conscious homeowners.”
Defined idea or theme: “Generate a five-line poem about the ocean in the style of Shakespeare.”
Specified structure: “In table format, compare the pros and cons of electric and gas vehicles.”
Research: Finding or connecting dots
Traditional research is time-consuming, requiring endless tab-switching and filtering through irrelevant results. AI-powered research can speed up our ability to uncover patterns and insights that might otherwise take hours. Research prompts can be incredibly powerful (even if you already have a base of knowledge).
Practical examples:
Cross-referencing multiple articles to build a more complete picture of a topic.
Understanding best practices around email marketing for an e-commerce brand.
Learning the difference between a novel’s inciting incident and its key incident.
What research prompts look like
Specific queries: “Find me five recent studies on AI ethics with summaries.”
Comparisons: “Compare the key differences between Apple's and Google’s AI strategies.”
Trend analysis: “Identify common complaints from 500 Amazon reviews of this product.”
Assimilation: Structuring and transforming information
Assimilation is about taking existing information and restructuring, formatting, or converting it into a different form that better meets our needs. Any given day, we consume information in a multitude of forms, whether that’s raw table data, lists, documents, audio or images. AI can bridge the gap, saving us hours of the most tedious of work.
Practical examples:
Turning a messy bulleted list into a structured table.
Extracting text from an image or pulling content from an HTML page.
Converting handwritten notes into a clean, organized summary.
What assimilation prompts look like
Transformation requests: “Convert this bulleted list into a well-organized table.”
Extraction commands: “Pull all text from this webpage and format it into a Markdown document.”
Reformatting prompts: “Take this long paragraph and turn it into a step-by-step guide.”
Summarization: Distilling information
Summarization helps reduce large amounts of content into its most important insights. With overwhelming amounts of information coming at us (did anyone see Bob’s last thread?), AI helps us extract what really matters.
Practical examples:
Summarizing a 30-page document into a concise, actionable summary.
Generating key takeaways from a long meeting transcript.
Condensing a news article into its most critical points.
Analyzing trends across hundreds of customer reviews to find recurring themes.
What summarization prompts look like
Length constraints: “Summarize this 10,000-word report in three bullet points.”
Format requests: “Generate a one-paragraph summary and a key takeaways section.”
Audience filtering: “Summarize this article for a 12-year-old audience.”
A real-world workflow: Bringing all four buckets together
The real power of AI isn’t in using just one of these buckets—it’s in combining them to streamline work and win back more time for decision-making. To see how these categories work together, let’s look at a real-world workflow where all four play a role.
Scenario: A podcaster prepares for an episode
Research: The podcaster asks AI to gather key points, recent news, and expert opinions on their topic.
Summarization: AI condenses lengthy articles, interviews, videos, and reports into key points.
Assimilation: The podcaster takes their wealth of summarized content and converts it into a uniformly structured outline.
Generation: AI drafts not only a script and show notes, but promotional blurbs and prep materials for guests.
Most people use AI backwards
Most people start with AI at the wrong end of the spectrum—generation. And who can blame them? It’s the visible, marketable, and, let’s be honest, fun route. Creating stunning visuals or generating code snippets feels… impactful.
But focusing only on creation means missing out entirely on how quickly AI processes information. Imagine trying to write a research paper without first gathering sources, or designing a product without studying user needs. But when you flip the process—research, summarization, assimilation, and then generation—we get more precise, informed, and efficient results.
So instead of treating it as a content factory, think of it as an intelligence engine. Less as a writer and more as an analyst. You’ll find true efficiency starts to come when you use AI to understand first, structure second, and generate last.
Instead of just reading about these four categories, test them yourself and see firsthand how these functions work together. Give this a try:
Find a long-form article that interests you (or use this one).
Summarize it using AI.
Have AI transform that summary into a structured table.
Use AI to generate a blog post based on the table’s key takeaways.
If you’re only using AI to generate content, you’re missing out on some of its most valuable capabilities.