{"id":108686,"date":"2026-04-30T12:20:43","date_gmt":"2026-04-30T19:20:43","guid":{"rendered":"https:\/\/www.crazyegg.com\/blog\/?p=108686"},"modified":"2026-04-30T12:20:44","modified_gmt":"2026-04-30T19:20:44","slug":"ai-blog-writing","status":"publish","type":"post","link":"https:\/\/www.crazyegg.com\/blog\/ai-blog-writing\/","title":{"rendered":"How I Use AI to Write Blog Posts (And What I Still Do by Hand)"},"content":{"rendered":"\n<p><a href=\"https:\/\/www.semrush.com\/blog\/does-ai-content-rank-in-search-data-study\/\" target=\"_blank\" rel=\"noreferrer noopener\">A recent Semrush study<\/a> found that 70% of marketers use AI to create content faster at a lower cost. Only 19% use it to improve content quality.\u00a0<\/p>\n\n\n\n<p>I&#8217;ve always been in the 19% camp. My main motivation for using AI isn&#8217;t to save time, but to write better blog content. That said, I understand the constraints that force marketers to use AI for speed.&nbsp;<\/p>\n\n\n\n<p>With that in mind, I designed the article to serve two purposes.<\/p>\n\n\n\n<p>I will show you my exact process for writing high-quality content with AI. You will learn where I lean into AI a lot and where I prefer to do things by hand \u2014 and why.&nbsp;<\/p>\n\n\n\n<p>I will also explain what I would do to get the best results if I were to automate the whole process.&nbsp;<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Step 1: Build the client context before anything else<\/h2>\n\n\n\n<p>The quality of AI output throughout the writing process depends on the context you share. So when I start working with a new client, I always build a comprehensive brand profile (brand kit). And I use AI to help me.&nbsp;<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">What does the brand kit contain?<\/h3>\n\n\n\n<p>The brand kit I generate covers:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>General brand information (product name, category, website, maturity)<\/li>\n\n\n\n<li>Key competitors<\/li>\n\n\n\n<li>Content templates (outlines, article samples, titles)<\/li>\n\n\n\n<li>Features, benefits, and use cases<\/li>\n\n\n\n<li>Positioning and unique selling propositions<\/li>\n\n\n\n<li>Case studies (more about it later)<\/li>\n\n\n\n<li>Target audience<\/li>\n<\/ul>\n\n\n\n<p>Based on content samples, Claude also creates:&nbsp;<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Brand voice and style guidance<\/li>\n\n\n\n<li>Outlining and drafting rules<\/li>\n\n\n\n<li>Internal linking rules<\/li>\n<\/ul>\n\n\n\n<p>I store it on my hard drive, and every later AI task uses the files for reference.<\/p>\n\n\n\n<p>Without it, AI tools revert to the generic writing tone and style, and the content fails to connect readers&#8217; pain points with the solutions the product offers.<\/p>\n\n\n\n<figure class=\"wp-block-image size-full\"><img decoding=\"async\" width=\"893\" height=\"331\" src=\"https:\/\/ceblog.s3.amazonaws.com\/wp-content\/uploads\/2026\/04\/30121121\/AI-Blog-Posts-Folder-Structure.png\" alt=\"File explorer showing the Crazy Egg client content folder structure.\" class=\"wp-image-108700\" srcset=\"https:\/\/ceblog.s3.amazonaws.com\/wp-content\/uploads\/2026\/04\/30121121\/AI-Blog-Posts-Folder-Structure.png 893w, https:\/\/ceblog.s3.amazonaws.com\/wp-content\/uploads\/2026\/04\/30121121\/AI-Blog-Posts-Folder-Structure-300x111.png 300w, https:\/\/ceblog.s3.amazonaws.com\/wp-content\/uploads\/2026\/04\/30121121\/AI-Blog-Posts-Folder-Structure-768x285.png 768w\" sizes=\"(max-width: 893px) 100vw, 893px\" \/><\/figure>\n\n\n\n<h3 class=\"wp-block-heading\">How do I collect the client information?<\/h3>\n\n\n\n<p>I start with anything the client sends me. Brand guidelines, style guides, positioning docs, guidance for freelancers. That&#8217;s the most reliable way to gather the information.&nbsp;<\/p>\n\n\n\n<p>I have also built a client research skill in Claude Code, which scrapes their:<\/p>\n\n\n\n<ol class=\"wp-block-list\">\n<li>Homepage<\/li>\n\n\n\n<li>Product pages (features, personas, use cases)<\/li>\n\n\n\n<li>Case studies<\/li>\n\n\n\n<li>Recent blog posts \u2014 multiple samples across formats<\/li>\n\n\n\n<li>Pricing page<\/li>\n\n\n\n<li>Review pages<\/li>\n\n\n\n<li>Support docs<\/li>\n<\/ol>\n\n\n\n<p>Claude saves the raw pages in relevant folders and can access them when performing tasks. It also extracts relevant information to build the brand kit and prepopulates an onboarding form I share with the client. Which they can check for accuracy and add relevant details.<\/p>\n\n\n\n<p><strong>Pro tip: <\/strong>If you don&#8217;t prompt Claude Code to <strong>scrape <\/strong>the page, it will usually summarize its content. The summaries aren&#8217;t always complete or accurate.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Step 2: Research With AI Tools Across Multiple Sources<\/h2>\n\n\n\n<p>Research is where AI makes the biggest impact. It doesn&#8217;t shorten my research time \u2014 it always takes more or less the same, whether I use AI or not. But it allows me to access more sources and gather more insights, so the content is deeper.&nbsp;<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Get a general topic understanding with NotebookLM Audio Overviews and Mindmaps<\/h3>\n\n\n\n<p>I start research by uploading the top-ranking articles to NotebookLM, the free AI notetaking tool from Google, and creating an Audio Overview.&nbsp;<\/p>\n\n\n\n<p>The audio overview sounds like a podcast where two folks discuss the topic. Something I can listen to while mowing the lawn, running, or driving. And it helps me get a general understanding of the topic before I start research.<\/p>\n\n\n\n<p>The best part?<\/p>\n\n\n\n<p>It&#8217;s based on the sources I give it. So I can control the quality, focus, and scope. This beats regular podcasts for this specific use case.<\/p>\n\n\n\n<figure class=\"wp-block-image size-full\"><img decoding=\"async\" width=\"1907\" height=\"900\" src=\"https:\/\/ceblog.s3.amazonaws.com\/wp-content\/uploads\/2026\/04\/30121152\/AI-Blog-Posts-NotebookLM.png\" alt=\"NotebookLM interface displaying a research notebook on mastering perceived value in digital design.\" class=\"wp-image-108701\" srcset=\"https:\/\/ceblog.s3.amazonaws.com\/wp-content\/uploads\/2026\/04\/30121152\/AI-Blog-Posts-NotebookLM.png 1907w, https:\/\/ceblog.s3.amazonaws.com\/wp-content\/uploads\/2026\/04\/30121152\/AI-Blog-Posts-NotebookLM-300x142.png 300w, https:\/\/ceblog.s3.amazonaws.com\/wp-content\/uploads\/2026\/04\/30121152\/AI-Blog-Posts-NotebookLM-1024x483.png 1024w, https:\/\/ceblog.s3.amazonaws.com\/wp-content\/uploads\/2026\/04\/30121152\/AI-Blog-Posts-NotebookLM-768x362.png 768w, https:\/\/ceblog.s3.amazonaws.com\/wp-content\/uploads\/2026\/04\/30121152\/AI-Blog-Posts-NotebookLM-1536x725.png 1536w\" sizes=\"(max-width: 1907px) 100vw, 1907px\" \/><\/figure>\n\n\n\n<p>Another NotebookLM feature I usually use is the mindmap, which gives me an overview of the key concepts and how they&#8217;re related to each other.&nbsp;<\/p>\n\n\n\n<figure class=\"wp-block-image size-full\"><img decoding=\"async\" width=\"680\" height=\"683\" src=\"https:\/\/ceblog.s3.amazonaws.com\/wp-content\/uploads\/2026\/04\/30121216\/AI-Blog-Posts-Value-Mindmap.png\" alt=\"Mind map showing the dimensions of perceived value and market psychology based on 11 sources.\" class=\"wp-image-108702\" srcset=\"https:\/\/ceblog.s3.amazonaws.com\/wp-content\/uploads\/2026\/04\/30121216\/AI-Blog-Posts-Value-Mindmap.png 680w, https:\/\/ceblog.s3.amazonaws.com\/wp-content\/uploads\/2026\/04\/30121216\/AI-Blog-Posts-Value-Mindmap-300x300.png 300w, https:\/\/ceblog.s3.amazonaws.com\/wp-content\/uploads\/2026\/04\/30121216\/AI-Blog-Posts-Value-Mindmap-150x150.png 150w\" sizes=\"(max-width: 680px) 100vw, 680px\" \/><\/figure>\n\n\n\n<h3 class=\"wp-block-heading\">Analyze SERP with Claude Code<\/h3>\n\n\n\n<p>While NotebookLM is generating the Audio Overview, Claude Code runs the SERP analysis.&nbsp;<\/p>\n\n\n\n<p>It uses a custom skill which:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Pulls the SERP results and PPA questions for the keyword from DataForSEO<\/li>\n\n\n\n<li>Analyzes the type of content that ranks and the search intent behind the keyword<\/li>\n\n\n\n<li>Selects and scrapes all blog posts<\/li>\n\n\n\n<li>Summarizes them section by section<\/li>\n\n\n\n<li>Extracts core problems, pain points, and questions the ranking articles address<\/li>\n\n\n\n<li>Identifies common patterns, assumptions, notable elements, and possible missing perspectives<\/li>\n\n\n\n<li>Analyzes the article&#8217;s structure and formatting<\/li>\n\n\n\n<li>Pulls and dissects relevant Reddit threads to find questions and pain points not covered in the ranking articles and suggests opportunities.<\/li>\n\n\n\n<li>Suggests an angle that allows showcasing the client&#8217;s product (grounded in the brand kit)<\/li>\n<\/ul>\n\n\n\n<p>Here&#8217;s what the section of the report looks like for a recent Crazy Egg article about <a href=\"https:\/\/www.crazyegg.com\/blog\/convert-by-adding-perceived-value\/\" target=\"_blank\" rel=\"noreferrer noopener\">perceived value<\/a>:<\/p>\n\n\n\n<figure class=\"wp-block-image size-full\"><img decoding=\"async\" width=\"1885\" height=\"694\" src=\"https:\/\/ceblog.s3.amazonaws.com\/wp-content\/uploads\/2026\/04\/30121244\/AI-Blog-Posts-Search-Intent.png\" alt=\"AI-generated search intent analysis for the keyword &quot;perceived value.&quot;\" class=\"wp-image-108703\" srcset=\"https:\/\/ceblog.s3.amazonaws.com\/wp-content\/uploads\/2026\/04\/30121244\/AI-Blog-Posts-Search-Intent.png 1885w, https:\/\/ceblog.s3.amazonaws.com\/wp-content\/uploads\/2026\/04\/30121244\/AI-Blog-Posts-Search-Intent-300x110.png 300w, https:\/\/ceblog.s3.amazonaws.com\/wp-content\/uploads\/2026\/04\/30121244\/AI-Blog-Posts-Search-Intent-1024x377.png 1024w, https:\/\/ceblog.s3.amazonaws.com\/wp-content\/uploads\/2026\/04\/30121244\/AI-Blog-Posts-Search-Intent-768x283.png 768w, https:\/\/ceblog.s3.amazonaws.com\/wp-content\/uploads\/2026\/04\/30121244\/AI-Blog-Posts-Search-Intent-1536x566.png 1536w\" sizes=\"(max-width: 1885px) 100vw, 1885px\" \/><\/figure>\n\n\n\n<h3 class=\"wp-block-heading\">Gather data with Deep Research<\/h3>\n\n\n\n<p>I use Claude and the SERP analysis report to create a deep research prompt.<\/p>\n\n\n\n<p>The prompt can easily be 2000+ words long. Here&#8217;s what a section of it looked like for the perceived value article:<\/p>\n\n\n\n<figure class=\"wp-block-image size-full\"><img decoding=\"async\" width=\"772\" height=\"572\" src=\"https:\/\/ceblog.s3.amazonaws.com\/wp-content\/uploads\/2026\/04\/30121310\/AI-Blog-Posts-Research-Prompt.png\" alt=\"Research prompt instructing an AI to gather evidence on perceived value for a Crazy Egg article.\" class=\"wp-image-108704\" srcset=\"https:\/\/ceblog.s3.amazonaws.com\/wp-content\/uploads\/2026\/04\/30121310\/AI-Blog-Posts-Research-Prompt.png 772w, https:\/\/ceblog.s3.amazonaws.com\/wp-content\/uploads\/2026\/04\/30121310\/AI-Blog-Posts-Research-Prompt-300x222.png 300w, https:\/\/ceblog.s3.amazonaws.com\/wp-content\/uploads\/2026\/04\/30121310\/AI-Blog-Posts-Research-Prompt-768x569.png 768w\" sizes=\"(max-width: 772px) 100vw, 772px\" \/><\/figure>\n\n\n\n<p>I use Perplexity and ChatGPT in Agent mode for deep research, and I run two searches: one searches my trusted sources (I build a list for each client) and a wide, unrestricted one.&nbsp;<\/p>\n\n\n\n<p>Deep research results are plagued with hallucinations, so I&#8217;ve embedded a fact-check loop in the process. It scrapes every source cited in the research reports and checks if the source exists, if it&#8217;s of the right quality (recent, primary research), and if the information was cited accurately.&nbsp;<\/p>\n\n\n\n<p>I still manually check all data and sources I use in an article, but this process leaves fewer to comb through, and the list is generally of better quality.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Find peer-reviewed sources and cross-reference ideas with Consensus<\/h3>\n\n\n\n<p>Consensus is an AI-powered platform for academic research. Ask it a question, Consensus looks for sources and provides a summary. It also assesses the quality of the sources and estimates how confident you can be in the search results.<\/p>\n\n\n\n<p>I use it to test claims and assumptions.<\/p>\n\n\n\n<p>For instance, when working on the perceived value piece, I came across the common belief that high NPS scores are linked to repeated purchases. But Consensus found that the correlation isn&#8217;t strong, and it depends on context, so I chose not to include it in the article.<\/p>\n\n\n\n<figure class=\"wp-block-image size-full\"><img decoding=\"async\" width=\"784\" height=\"708\" src=\"https:\/\/ceblog.s3.amazonaws.com\/wp-content\/uploads\/2026\/04\/30121337\/AI-Blog-Posts-NPS-Research.png\" alt=\"Perplexity AI search results showing research on NPS and repeat purchase likelihood.\" class=\"wp-image-108705\" srcset=\"https:\/\/ceblog.s3.amazonaws.com\/wp-content\/uploads\/2026\/04\/30121337\/AI-Blog-Posts-NPS-Research.png 784w, https:\/\/ceblog.s3.amazonaws.com\/wp-content\/uploads\/2026\/04\/30121337\/AI-Blog-Posts-NPS-Research-300x271.png 300w, https:\/\/ceblog.s3.amazonaws.com\/wp-content\/uploads\/2026\/04\/30121337\/AI-Blog-Posts-NPS-Research-768x694.png 768w\" sizes=\"(max-width: 784px) 100vw, 784px\" \/><\/figure>\n\n\n\n<p><strong>Pro tip: <\/strong>Claude offers a Consensus connector, so you can run searches directly from your terminal or desktop app.&nbsp;<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Search podcasts and videos with Gemini or NotebookLM&nbsp;<\/h3>\n\n\n\n<p>Podcasts and videos were traditionally hard to analyze. You either had to listen to the whole recording or transcribe it with a 3rd-party tool and work with the transcript. Both required time and\/or resources.<\/p>\n\n\n\n<p>AI has made them more accessible.&nbsp;<\/p>\n\n\n\n<p>Gemini, which powers NotebookLM, is good at multimodal processing, and this includes extracting ideas from audio-visual content, so I use it to find relevant episodes or videos.<\/p>\n\n\n\n<p>You can do either manually, by pasting the link into the Gemini chat or NotebookLM, and use Claude with the Gemini API.&nbsp; Here&#8217;s a section of a video summary about perceived value.&nbsp;<\/p>\n\n\n\n<figure class=\"wp-block-image size-full\"><img decoding=\"async\" width=\"707\" height=\"595\" src=\"https:\/\/ceblog.s3.amazonaws.com\/wp-content\/uploads\/2026\/04\/30121404\/AI-Blog-Posts-YouTube-Summary.png\" alt=\"Gemini AI summary of a YouTube video on increasing perceived value without lowering prices.\" class=\"wp-image-108706\" srcset=\"https:\/\/ceblog.s3.amazonaws.com\/wp-content\/uploads\/2026\/04\/30121404\/AI-Blog-Posts-YouTube-Summary.png 707w, https:\/\/ceblog.s3.amazonaws.com\/wp-content\/uploads\/2026\/04\/30121404\/AI-Blog-Posts-YouTube-Summary-300x252.png 300w\" sizes=\"(max-width: 707px) 100vw, 707px\" \/><\/figure>\n\n\n\n<p>Two caveats:&nbsp;<\/p>\n\n\n\n<p>First, I&#8217;ve seen Gemini hallucinate. Badly. When it can&#8217;t extract the information you need, it doesn&#8217;t tell you, just invents it. So once I have the video or podcast I want to use, I still transcribe it \u2014 or listen to it. But now only the relevant ones.<br><br>Second,<strong> <\/strong>LLMs can choose relevant quotes from a transcript, but I&#8217;ve found they aren&#8217;t always the best ones. So, I always prompt them to list all relevant ones, and I make the final call.<\/p>\n\n\n\n<p><strong>Pro tip:<\/strong> Claude can also find relevant YouTube videos and pull their subtitles with DataForSEO MCP.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Tap into internal sources&nbsp;<\/h3>\n\n\n\n<p>Using clients&#8217; sources, like case studies or internal data, is the easiest way to differentiate content because competitors don&#8217;t have them, or can&#8217;t use them without citing you.<\/p>\n\n\n\n<p>I used to create a NotebookLM file with all clients&#8217; assets, so I could query them when researching.<\/p>\n\n\n\n<p>Now, they all live in .md files in the client&#8217;s knowledge base \u2014 along with industry reports and competitor profiles \u2014 ready to use for Claude.&nbsp;<\/p>\n\n\n\n<figure class=\"wp-block-image size-full\"><img decoding=\"async\" width=\"969\" height=\"294\" src=\"https:\/\/ceblog.s3.amazonaws.com\/wp-content\/uploads\/2026\/04\/30121432\/AI-Blog-Posts-Knowledge-Base.png\" alt=\"File explorer showing the Crazy Egg knowledge base subfolders including case studies and competitor profiles.\" class=\"wp-image-108707\" srcset=\"https:\/\/ceblog.s3.amazonaws.com\/wp-content\/uploads\/2026\/04\/30121432\/AI-Blog-Posts-Knowledge-Base.png 969w, https:\/\/ceblog.s3.amazonaws.com\/wp-content\/uploads\/2026\/04\/30121432\/AI-Blog-Posts-Knowledge-Base-300x91.png 300w, https:\/\/ceblog.s3.amazonaws.com\/wp-content\/uploads\/2026\/04\/30121432\/AI-Blog-Posts-Knowledge-Base-768x233.png 768w\" sizes=\"(max-width: 969px) 100vw, 969px\" \/><\/figure>\n\n\n\n<p><strong>Pro tip: <\/strong>Storing client resources and research in a vector database, like ChromaDB, makes them easier for LLMs to search.&nbsp;<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Automate the research pipeline&nbsp;<\/h3>\n\n\n\n<p>You can automate the research process by chaining individual research workflows into one master research workflow.<\/p>\n\n\n\n<p>I&#8217;ve built such workflows in AirOps, but you can do it in Claude Code (it&#8217;s just harder to keep track of progress without the visual interface)<\/p>\n\n\n\n<figure class=\"wp-block-image size-full\"><img decoding=\"async\" width=\"1695\" height=\"498\" src=\"https:\/\/ceblog.s3.amazonaws.com\/wp-content\/uploads\/2026\/04\/30121453\/AI-Blog-Posts-Research-Workflow.png\" alt=\"Workflow tool showing multiple parallel AI research pipeline columns for blog post production.\" class=\"wp-image-108708\" srcset=\"https:\/\/ceblog.s3.amazonaws.com\/wp-content\/uploads\/2026\/04\/30121453\/AI-Blog-Posts-Research-Workflow.png 1695w, https:\/\/ceblog.s3.amazonaws.com\/wp-content\/uploads\/2026\/04\/30121453\/AI-Blog-Posts-Research-Workflow-300x88.png 300w, https:\/\/ceblog.s3.amazonaws.com\/wp-content\/uploads\/2026\/04\/30121453\/AI-Blog-Posts-Research-Workflow-1024x301.png 1024w, https:\/\/ceblog.s3.amazonaws.com\/wp-content\/uploads\/2026\/04\/30121453\/AI-Blog-Posts-Research-Workflow-768x226.png 768w, https:\/\/ceblog.s3.amazonaws.com\/wp-content\/uploads\/2026\/04\/30121453\/AI-Blog-Posts-Research-Workflow-1536x451.png 1536w\" sizes=\"(max-width: 1695px) 100vw, 1695px\" \/><\/figure>\n\n\n\n<p>Thanks to this, I can run all the steps at once for multiple keywords.<\/p>\n\n\n\n<p>This speeds up research, which matters when creating content at scale,&nbsp; but comes with a trade-off: you can&#8217;t chase unexpected threads that you discover, so the depth and nuance suffer.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Step 3: Outline the Article Manually With AI Assistance<\/h2>\n\n\n\n<p>With all the research in place, I build a granular outline.&nbsp;<\/p>\n\n\n\n<p>Manually, with AI assistance.<\/p>\n\n\n\n<p>And not the other way round. Which is just the opposite of what a lot of content creators seem to be doing.<\/p>\n\n\n\n<p>Why not use AI for outlining? Here are a few reasons:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>The outline is the single most important part of the process. It makes or breaks the article, so I want maximum control.<\/li>\n\n\n\n<li>The outline is my opportunity to give the article a unique, personal angle. AI-generated outlines follow the same structure as everything else in the SERP.\u00a0<\/li>\n\n\n\n<li>AI isn&#8217;t great at using the research outputs to build logical arguments. For example, it fails at transitive relevance (paragraph A logically leads to paragraph B, B logically leads to C, but C has nothing to do with A or the main article premise).<\/li>\n<\/ul>\n\n\n\n<p>Also, outlining is the most intellectually satisfying part of writing for me, and I don&#8217;t want to give it up. Especially, if there&#8217;s a real risk of losing the skills in the process, as <a href=\"https:\/\/arxiv.org\/pdf\/2604.04721\" target=\"_blank\" rel=\"noreferrer noopener\">research<\/a> shows.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Use AI for feedback<\/h3>\n\n\n\n<p>Interestingly enough, AI is pretty good at <em>spotting <\/em>logical flaws, gaps, and inconsistencies, so I use it for feedback.&nbsp;<\/p>\n\n\n\n<p>My structural outline edit skill checks if the outline:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Includes only relevant ideas<\/li>\n\n\n\n<li>Is MECE (Mutually Exclusive, Collectively Exhaustive): no overlaps between sections which cover the topic comprehensively<\/li>\n\n\n\n<li>Follow the pyramid principle\/BLUFs? Key idea up front, supporting information later.<\/li>\n\n\n\n<li>Uses data to logically support the argument<\/li>\n\n\n\n<li>Has appropriately weighted sections<\/li>\n\n\n\n<li>Delivers on the promises in the title\/section headers<\/li>\n\n\n\n<li>Has concept-dense, grammatically parallel headers<\/li>\n<\/ul>\n\n\n\n<p>It offers suggestions, but I never let it implement them. I fix everything manually.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">If you really must use AI for outlining<\/h3>\n\n\n\n<p>If I still want to build an outlining workflow, here&#8217;s how to get better outputs:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Provide outline samples (extract outlines from existing articles)<\/li>\n\n\n\n<li>Prompt it to build the outline based on all the research findings, not just SERP.\u00a0<\/li>\n\n\n\n<li>Instruct it to include all relevant stats it found; don&#8217;t let it pick. Make it explain how each one is relevant.<\/li>\n\n\n\n<li>Stay in the loop \u2014 check the AI output thoroughly<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\">Step 4: Draft the Article Manually (Unless the Outline Is Airtight)<\/h2>\n\n\n\n<p>If the outline is strong and it has enough context, AI can produce a decent draft. And yet, I still write all high-impact content manually.<\/p>\n\n\n\n<p>Let&#8217;s break it down.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Provide context and detailed instructions<\/h3>\n\n\n\n<p>The quality of LLM output depends on the context and detailed instructions. So the brand kit we built in Step 1, plus granular writing rules.&nbsp;<\/p>\n\n\n\n<p>I have two sets of rules:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>General writing rules<\/strong> that apply to all content I draft. For example, &#8220;Don&#8217;t restate the same idea in different words within a section.&#8221;<\/li>\n<\/ul>\n\n\n\n<figure class=\"wp-block-image size-full\"><img decoding=\"async\" width=\"1848\" height=\"154\" src=\"https:\/\/ceblog.s3.amazonaws.com\/wp-content\/uploads\/2026\/04\/30121548\/AI-Blog-Posts-Writing-Rule.png\" alt=\"Writing rule reminding authors not to restate the same idea across paragraphs.\" class=\"wp-image-108709\" srcset=\"https:\/\/ceblog.s3.amazonaws.com\/wp-content\/uploads\/2026\/04\/30121548\/AI-Blog-Posts-Writing-Rule.png 1848w, https:\/\/ceblog.s3.amazonaws.com\/wp-content\/uploads\/2026\/04\/30121548\/AI-Blog-Posts-Writing-Rule-300x25.png 300w, https:\/\/ceblog.s3.amazonaws.com\/wp-content\/uploads\/2026\/04\/30121548\/AI-Blog-Posts-Writing-Rule-1024x85.png 1024w, https:\/\/ceblog.s3.amazonaws.com\/wp-content\/uploads\/2026\/04\/30121548\/AI-Blog-Posts-Writing-Rule-768x64.png 768w, https:\/\/ceblog.s3.amazonaws.com\/wp-content\/uploads\/2026\/04\/30121548\/AI-Blog-Posts-Writing-Rule-1536x128.png 1536w\" sizes=\"(max-width: 1848px) 100vw, 1848px\" \/><\/figure>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Brand-specific rules.<\/strong> For example, &#8221; Write short paragraphs. Two to three sentences is the target. One-sentence paragraphs are fine for emphasis. Never write paragraphs longer than 5 sentences.&#8221;<\/li>\n<\/ul>\n\n\n\n<p>I&#8217;ve built the rules based on samples of good content and general writing principles. And hours of developmental feedback on specific draft sections.&nbsp;&nbsp;<\/p>\n\n\n\n<p><strong>Pro tip: <\/strong>When your LLM doesn&#8217;t follow the rules, check for conflicts between different sets of rules or prompts. Removing conflicting instructions often works better than adding more exceptions.&nbsp;<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Why I draft manually<\/h3>\n\n\n\n<p>There are a few reasons why I write all drafts by hand:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Plagiarism risk: <\/strong>I&#8217;ve seen AI copy the arguments, paragraph structure, examples, or analogies from other articles. Not something a plagiarism checker would pick up, but you know immediately when you see the two side by side. Writing manually is the only way to avoid it.<\/li>\n\n\n\n<li><strong>Audience perception. <\/strong><a href=\"https:\/\/www.crazyegg.com\/blog\/ai-marketing-assets\/\" target=\"_blank\" rel=\"noreferrer noopener\">AI aversion<\/a> is a well-documented concept. Readers devalue AI-written content regardless of its quality. And it&#8217;s not difficult to get accused of it these days.\u00a0<\/li>\n\n\n\n<li><strong>AI writes without personality,<\/strong> which is becoming a key differentiator.\u00a0<\/li>\n\n\n\n<li><strong>Finding factual or logical errors in AI content is hard without subject-matter knowledge.<\/strong> I don&#8217;t write things I don&#8217;t understand thoroughly first, so I won&#8217;t make such errors.<\/li>\n<\/ul>\n\n\n\n<p>So even if my clients didn&#8217;t insist on 100% human-written drafts, I would still be reluctant to let AI do the work.&nbsp;<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Step 5: Edit With AI, Sign Off Manually<\/h2>\n\n\n\n<p>AI helps reduce my editing, often by 70-80%, and is more consistent than I am, but the final pass is always mine.<\/p>\n\n\n\n<p>I have built three editing workflows:<\/p>\n\n\n\n<ol class=\"wp-block-list\">\n<li><strong>High-level edit.<\/strong> Checks alignment with the outline and brief, flow of ideas, section structure. Flags fallacies, unsupported claims, factual inaccuracies \u2014 things a skeptical reader would question.<\/li>\n\n\n\n<li><strong>Line edit.<\/strong> Removes clich\u00e9s, tautologies, unnecessary passive voice, fluff, repetition.<\/li>\n\n\n\n<li><strong>Humanizer.<\/strong> Strips language patterns associated with AI, like contrasting pairs or participial clauses (because I sometimes use them myself).\u00a0<\/li>\n<\/ol>\n\n\n\n<p>I update these regularly based on client feedback.&nbsp;<\/p>\n\n\n\n<p>For consistent performance, I&#8217;ve built feedback loops into each step. Agent A does the edit, Agent B checks its work, fixes what A missed, and passes it back to A. Rinse and repeat until they tick all boxes.<\/p>\n\n\n\n<figure class=\"wp-block-image size-full\"><img decoding=\"async\" width=\"701\" height=\"301\" src=\"https:\/\/ceblog.s3.amazonaws.com\/wp-content\/uploads\/2026\/04\/30121621\/AI-Blog-Posts-Quality-Loop.png\" alt=\"Diagram showing a ping-pong quality loop between Agent A and Agent B for iterative content review.\" class=\"wp-image-108710\" srcset=\"https:\/\/ceblog.s3.amazonaws.com\/wp-content\/uploads\/2026\/04\/30121621\/AI-Blog-Posts-Quality-Loop.png 701w, https:\/\/ceblog.s3.amazonaws.com\/wp-content\/uploads\/2026\/04\/30121621\/AI-Blog-Posts-Quality-Loop-300x129.png 300w\" sizes=\"(max-width: 701px) 100vw, 701px\" \/><\/figure>\n\n\n\n<p>Speaking of box-ticking: All these tasks are simple checklist items, and AI is good at following checklists.<\/p>\n\n\n\n<p>However, an experienced editor often makes calls based on their personal judgement \u2014 or taste. AI has no taste, and it&#8217;s impossible to code it into the writing rules. Which is why I do another pass and make the final edits manually.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Final Thoughts<\/h2>\n\n\n\n<p>You can use AI for all stages of blog writing: from research, through outlining, through drafting, through editing.&nbsp;<\/p>\n\n\n\n<p>If time and budget matter, you can run the whole pipeline with a single click, but be ready for subpar quality.<\/p>\n\n\n\n<p>If quality matters, use it for research and editing, but don&#8217;t let it outline or draft without close supervision from a professional writer or editor.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>A recent Semrush study found that 70% of marketers use AI to create content faster at a lower cost. Only 19% use it to 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