NEW! Notion AI: Everything You Need to Know

TL;DR

Notion AI is in Alpha with waiting-list access on a first-come, first-served basis, and it’s free during Alpha while future pricing is unknown.

Briefing Cornell Notes

Briefing

Notion AI is rolling out as an Alpha feature that turns everyday writing, formatting, and data work inside Notion into AI-assisted workflows—ranging from summarizing and rewriting text to generating tables, code, emails, job descriptions, and even structured sample databases. Access is limited: it’s available via a waiting list on a first-come, first-served basis, and while the Alpha is free, future pricing is not yet disclosed. The underlying model appears to be based on OpenAI’s GPT-3, setting expectations for text generation and transformation capabilities.

The most immediate value comes from AI actions embedded directly into Notion blocks. Users can select a block or highlight text and then choose AI options such as “Summarize,” “Continue writing,” “Help me write,” “Help me edit,” “Translate,” and “AI assist” tools like “Fix spelling and grammar.” The workflow is designed to be incremental: a user can generate a summary at the top of a page, then press “continue writing” to expand details, or highlight a section and request a tighter rewrite (including converting content into bullet points). Translation is also block-level—highlight text and translate it into another language, with options to replace the original or insert the translation below.

Beyond prose, Notion AI supports structured outputs that can be converted into databases. For example, a user can ask for a table that lists functions used in a Notion formula with descriptions and syntax, then convert that table into a database for easier filtering and reuse. The transcript also flags a practical limitation: AI output can be inaccurate or misleading, so Notion includes a feedback mechanism where users can describe how results should be improved to boost accuracy.

The feature extends into content production and workplace tasks. It can generate blog posts, outlines, pros-and-cons lists, social media drafts (including Twitter-style promotions), and sales emails with subject lines and templated sections. For business operations, it can draft meeting agendas, job descriptions with specified requirements (like education and years of experience), and essay drafts on topics such as the mental-health impact of “productivity culture,” including the ability to add missing sections via “continue writing.”

Notion AI also handles calculations and formatting for technical work. Users can highlight a question about compounding interest and request a calculation, then convert results into inline KaTeX-style math for embedding in Notion. For coding, prompts can generate HTML/CSS/JavaScript skeletons (such as a login screen), and “continue writing” helps maintain the same code structure across files.

Finally, the AI can pull information from the web into tables—such as generating a list of U.S. presidents and their years served—then convert those tables into databases for sorting and filtering. It can also generate synthetic sample data for templates, like task-manager rows with columns for dates, descriptions, and checkbox “done” states. Overall, Notion AI is positioned as a block-by-block assistant that turns unstructured ideas into structured, reusable Notion content, while still requiring human review for correctness.

Cornell Notes

Notion AI (currently in Alpha) adds AI-assisted editing and generation directly inside Notion blocks, letting users summarize, rewrite, translate, and continue drafts with simple commands like “Summarize,” “Continue writing,” and “Help me edit.” It also produces structured outputs—tables that can be converted into databases—useful for formulas, sample data, and information scraped from the web (e.g., U.S. presidents and years served). For workplace use, it drafts meeting agendas, sales emails, job descriptions, and essays, and it can generate code skeletons for HTML/CSS/JavaScript. Because outputs can be inaccurate, Notion includes a feedback option to improve future results. Access requires joining a waiting list; Alpha is free, while future pricing is not yet known.

How does Notion AI work at the block level, and what are the most common actions?

AI features appear in the block menu and toolbar after selecting a block or highlighting text. Common actions include “Summarize” (to generate a condensed version of existing text), “Continue writing” (to expand a draft), “Help me write” (to generate new content from a prompt), “Help me edit” (to rewrite or improve selected text), and “Translate” (to convert highlighted text into another language such as Spanish). Users can either replace the original text or insert the AI output below the selection.

What’s the practical advantage of generating tables and converting them into databases?

Tables let AI output be structured into rows and columns that Notion can convert into databases. That enables downstream operations like filtering, sorting, and quickly reusing the data. Examples include generating a formula “function breakdown” table (with description and syntax) and converting it into a database, or scraping information into a table (like U.S. presidents and years served) and then sorting ascending/descending after converting to a database.

Where does accuracy risk show up, and how can users respond?

AI output can be inaccurate or misleading, especially for technical details. Notion includes a feedback box where users can describe how the output should be improved. That feedback is intended to help improve accuracy over time, so users should review results—particularly for formulas, calculations, and factual lists.

How does Notion AI support technical writing and math inside Notion?

For calculations, users can highlight a question about compounding interest and request a calculation; the AI returns the computed result in text. Users can then convert the result into inline math using KaTeX-style formatting (the transcript mentions wrapping equations with dollar signs). For technical content, AI can also generate code skeletons (HTML/CSS/JavaScript) and use “continue writing” to keep file structure consistent.

What workplace and marketing outputs can be generated from prompts?

Notion AI can draft meeting agendas (e.g., marketing strategies and user research), sales emails with a subject line and structured sections (greeting, body, and sign-off), job descriptions with specified requirements (such as education and years of experience), and essays on specific prompts (like the mental-health harms of productivity culture). It can also create social media promotions and pros-and-cons lists to support decision-making.

How can Notion AI help create template-ready sample data?

Users can ask for random or synthetic data in table form by specifying columns and desired content. For instance, a task-manager example can be generated with columns like date, description, and a checkbox “done” field. The AI fills example rows, and users can then convert the header row into database properties (including checkbox properties) to produce a ready-to-use template dataset.

Review Questions

What block-level AI actions in Notion can replace or insert rewritten content, and how do they differ from “continue writing”?
Why does converting AI-generated tables into databases matter for filtering and reuse?
What steps should users take when AI output might be inaccurate, especially for technical or factual tasks?

Key Points

1
Notion AI is in Alpha with waiting-list access on a first-come, first-served basis, and it’s free during Alpha while future pricing is unknown.
2
AI tools appear inside Notion block menus and toolbars, enabling summarization, rewriting, translation, and incremental drafting.
3
Notion AI can generate tables that can be converted into databases, making structured outputs reusable for sorting, filtering, and template building.
4
A built-in feedback mechanism lets users flag inaccurate or misleading AI outputs to improve results over time.
5
Notion AI supports workplace deliverables like meeting agendas, sales emails, job descriptions, and essay drafts, including adding missing sections via “continue writing.”
6
Technical workflows include calculation requests, inline math formatting using KaTeX-style syntax, and code skeleton generation for HTML/CSS/JavaScript.
7
Information scraping and synthetic data generation can produce table-based datasets (e.g., U.S. presidents or task-manager sample rows) that become databases inside Notion.

Highlights

Notion AI turns highlighted text into new versions—summaries, rewrites, translations—and can either replace the original or insert the output below it.

Tables generated by AI can be converted into databases, enabling sorting and filtering on structured data like scraped lists or formula breakdowns.

A feedback box is built in to correct inaccurate or misleading AI results, acknowledging that outputs may not always be reliable.

The feature supports technical formatting, including inline KaTeX-style equations, and can generate HTML/CSS/JavaScript skeletons that preserve file structure with “continue writing.”

AI can generate both factual-style tables (like U.S. presidents and years served) and synthetic sample datasets for templates (like task-manager rows with checkbox states).

Topics

Notion AI Alpha
Block-Level AI Tools
Tables to Databases
Workplace Templates
Inline Math and Code Generation

Mentioned

GPT-3