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Writing & Editing

Draft, revise, sharpen, and review text without losing your own voice.

31 stories (15 articles · 16 videos)

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A few good first pieces before you browse the full feed.

AI as your personal editor: fix any writing in under a minute

Three small prompts that turn any AI into a careful, fast editor — without it rewriting you into someone you are not. One pass each for clarity, tone, and grammar.

Writing with AI without sounding like AI

AI writing has a smell. Once you know what it is, you can stop producing it. Six concrete tells, six fixes, and a workflow that uses AI to make your writing better without making it sound like everyone else's.

AI for job hunting: CVs, cover letters, and interview prep

A practical AI workflow for the job hunt — tailoring your CV to each role, drafting cover letters that sound like you, rehearsing interviews out loud, and the steps most candidates skip.

More in this topic

Draft First, Then Ask for Critique - Not Co-Authorship

The strongest AI assist for makers is usually critique of a draft you already wrote - not a co-authored first draft that quietly replaces your voice. A draft-then-critique loop with validation rules and a clear fallback when the feedback is generic.

An Edit Pass That Preserves Your Taste

AI edit passes often sand your work toward the mean. A taste-preserving edit loop: criteria first, local suggestions only, side-by-side keep/reject, and a final unaided read that protects what makes the piece yours.

Writing Through Grief Without Simulating the Person You Lost

AI can help you write a eulogy from your own memories, organize a funeral program, or draft a thank-you note when you cannot find the words. It should never be asked to generate what your person would say back to you. A safe-use table separates administrative and writing help from impersonation, with no exceptions for how comforting the impersonation feels.

Using AI to read long documents, contracts, and PDFs

Drop in a 40-page document and get the parts that matter — the decisions, the risks, the things you would otherwise miss. A practical workflow for using AI to read what you do not have time to read.

The difference between a bad and good prompt (5 before/after examples)

Five real prompts shown in their weak version and their better version, with a short note on what changed. The fastest way to upgrade your AI output without learning any jargon.

Using ChatGPT to write emails you actually want to send

Most AI emails sound like AI. A practical workflow for using ChatGPT to draft, sharpen, and finish the emails you would actually press send on — without the robot smell.

Deep Research mode: a 20-page report without reading 50 tabs

Deep Research — the autonomous research feature in ChatGPT, Gemini, Claude, and Perplexity — produces in fifteen minutes what used to take you a day. A practical guide to using it well.

Few-shot prompting: teach AI by example

When you cannot describe what you want, show it. Few-shot prompting — giving the model 2-3 examples of the output style — is the single fastest way to get consistent, on-brand results.

Inbox Zero with AI: a realistic email workflow

A practical, repeatable system for triaging, drafting, and chasing email with AI — without needing a developer, an automation builder, or a productivity guru.

NotebookLM: turn any source into a personal knowledge base

Google's NotebookLM is the easiest way to chat with your own documents — books, papers, meeting notes, research files. A practical tour of what it does, where it shines, and the four use cases worth setting up this week.

Stop prompting once: the iterative conversation method

The single biggest gap between mediocre and good AI output is what happens after the first response. A workflow for turning any first draft into a real one through iteration — not by re-prompting from scratch.

The ten prompt patterns every knowledge worker should know

Ten reusable patterns — not one-off prompts, but reusable shapes — that cover the vast majority of practical AI use at work. Memorise these and you have an entire prompt-engineering toolkit.

Land a Job using ChatGPT: The Definitive Guide!

Jeff Su. Ten minutes of the most-watched ChatGPT-for-job-search video on YouTube, and it earns it. Jeff shows the multi-prompt approach for tailoring a cover-letter hook to a real job description, then the "rewrite this bullet as I accomplished X by doing Y resulting in Z" pattern for resumes, then a clean walk-through of preparing the "tell me about yourself" answer. The exact moves the article recommends, demonstrated on screen with the prompts visible.

I wish every Job Seeker would watch this

Jeff Su. Part two of the same series, covering the parts of the job search the first video doesn't — landing coffee chats via LinkedIn, asking good questions in the interview, writing thank-you emails, and the role-play prompt for practising salary negotiation. Watch this after the article and you'll have ChatGPT-assisted scripts for every awkward part of the funnel, not just the application itself.

The Ultimate Guide to Writing with AI

David Perell. David Perell is a working writer who teaches writing online, and this is his actual workflow — where he asks AI to argue with his draft, where he refuses to let it generate prose for him, and what prompts he keeps coming back to. The sections "How do I actually write with LLMs" and "How I think with LLMs" map directly onto the article's "use AI as an editor, not a ghostwriter" stance. The view count is just under our usual 100k bar; we made the exception because no larger-channel video on this exact topic is anywhere near this thoughtful.

This NotebookLM + Perplexity Workflow Will Cut Your Research Time by 50% (or More)

Grace Leung. A more practical two-tool workflow: Perplexity for finding the documents and citations, NotebookLM for actually reading and synthesising them. Useful if your "long document" problem is really a "long stack of documents" problem — market research, regulatory filings, multi-source reports.

How to Use NotebookLM (Google's AI "Tool for Understanding")

Tiago Forte. NotebookLM is the AI tool the article most enthusiastically recommends for long documents, and Tiago Forte — the Building a Second Brain guy — gives the cleanest tour of why. He demonstrates summarising meeting notes, querying long PDFs, and the source-grounding feature that stops the model from inventing facts the documents don't contain. After watching, you'll understand why "upload the PDF to NotebookLM" is the article's default suggestion.

Prompt Engineering Tutorial – Master ChatGPT and LLM Responses

freeCodeCamp.org. A longer, more patient course that earns its length. The chapters on best practices, zero-shot vs few-shot, and what hallucinations look like in prompted output are exactly the deepening you want once the article's five examples have clicked.

Google's 9 Hour AI Prompt Engineering Course In 20 Minutes

Tina Huang. Tina condenses Google's official Prompting Essentials course into the five-part TCREI structure (Task, Context, References, Evaluate, Iterate) with worked examples on each. After the article shows you five rewrites, this hands you the recipe Google itself teaches employees, so you can do the rewrite on your own prompts without copying ours.

4 ChatGPT Hacks that Cut My Workload in Half

Jeff Su. The "red team technique" section is the missing piece in most ChatGPT-for-email tutorials: write the draft, then ask the model to roleplay the recipient who gets fifty cold emails a day and tell you which sentences make them hit delete. That feedback loop is exactly what the article means by "emails you actually want to send," and Jeff demonstrates it on a real cold outreach email in under three minutes.

AI For Data Analysis In 21 Minutes

Tina Huang. Tina puts Perplexity's Deep Research and ChatGPT/Claude analysis modes in the context of a working analyst's day — pulling together market signals, exploratory data analysis, and structured outputs. Useful as the "what else is this good for besides academic literature reviews" companion to the primary pick.

How to Use ChatGPT's Deep Research to Save HOURS on Research

Andy Stapleton. Stapleton runs Deep Research against the exact use case it shines at — a literature review with a clear scope, time frame, and structure — and shows the clarifying-questions handshake, the 10-minute wait, the 40-source output, and where the citations and tone still need a human pass. Calls out the limits honestly (no BibTeX export, occasionally too generous in source choice).

Prompt Engineering Tutorial – Master ChatGPT and LLM Responses

freeCodeCamp.org. Skip to the "Zero shot and few shot prompts" chapter at 31:20 for a clean, demoed comparison of the same task with no examples versus a few — exactly the contrast the article builds the chapter around. The earlier sections on prompt mindset and best practices are useful background if you've come straight from the article without the wider context.

Prompting 101 | Code w/ Claude

Anthropic. Examples are one of the ten ingredients Anthropic walks through here, and you can watch them get added to a real customer prompt and change the model's output. Useful if you want to see few-shot used inside a fuller prompt rather than as an isolated trick.

How I use LLMs

Andrej Karpathy. Karpathy walks through his actual day-to-day use across ChatGPT, Claude, Gemini, Grok, and Perplexity, screen-sharing live conversations rather than polished one-shots. You see when he reaches for a follow-up, when he switches models mid-thread, and when he abandons a line and starts over — exactly the iterative loop the article describes, modeled by someone who builds these systems for a living.

Master the Perfect ChatGPT Prompt Formula (in just 8 minutes)!

Jeff Su. Six-part formula — task, context, exemplars, persona, format, tone — explained with the same office-work examples the article uses (workout plans, resumes, internal emails). Watch this first; almost every pattern in the article is a focused application of one of these six components.

Every Essential AI Skill in 25 Minutes (2025)

Tina Huang. The prompting chapter (02:30–09:20) lays out two stackable mnemonics — "tiny crabs ride enormous iguanas" (task, context, references, evaluate, iterate) and "ramen saves tragic idiots" (revisit, separate, try analogous, introduce constraints) — that map cleanly onto the iterate-and-refine patterns in the article. Useful when a single-shot formula isn't getting you there.

The Ultimate Guide to Writing with AI

David Perell. Perell teaches writing for a living and walks through how he actually drafts with LLMs without the output reading as AI — what he uses the model for (research, structuring, surfacing his own thinking), what he refuses to delegate (voice, taste, opinions), and the specific moves he uses to keep his prose his. Sits just under the 100K-view bar but earns its place because the topic is genuinely niche and this is the most thoughtful long-form take on it.