Outlines

Outlines AI

Outlines AI

Outlines AI

Outlines AI

Outlines AI

Outlines AI

Outlines AI

Outlines AI

Outlines AI

Outlines AI

The rubber duck that talks back, Claude as editor

So the joke is explain your problem to a rubber duck and you'll figure out your problem when outlining it. Bewildered coworkers you enlisted and thank while still confused are living rubber ducks. Autocorrect keeps making it rubber dicks and now I want to call this dildo method lol. I'm editing a fairly dense piece of writing. I don't let it write for me because the writing is literally the average of the data. Acceptable but not exceptional. But the criticism does land. If it calls out an area as under supported lacking receipts I can see it and arguing back and forth will help me see flaws. Most of the time my logic is right and well did it actually make it into the document? No? Well, put it there! There's a lot of hate directed at ai in creative spaces and for generating the output I get it. That's putting people out or work. But for challenging and working as a partner, I think there's value. It's basically the same result if I had a human editor to pester at all hours but that's hard to come by. A human is ideal but it they are not available, the result is better than what I would do on my own. I will caveat you do need to be skeptical. It can false trigger but this is useful as well. It forces you to defend your ideas. Same as with human critics. And if you keep getting the same signal in new chats there's probably a flaw. I still consider human feedback the gold standard but this process helps you make sure you take care of easy flaws and let them diagnose issues that only humans can catch. submitted by /u/jollyreaper2112 [link] [comments]

Opus 4.8 hallucinates being in game it was designing

submitted by /u/Limp-Ad-6842 [link] [comments]

Overnight autonomous coding

At work we've been prompted about running Claude Code overnight. The suggestion came in form of a document that loosely outlined how this could be done... use git worktrees, make tight specs, no commit to main, static code analysis and lining etc. Very high level. Had a bit of sales pitch smell to it, but has enough content to peak my interest in spite of it. I looked at reddit to verify if this is even an idea that could be taken seriously. I could only find a couple of reddit posts with little actual information and usually from about 4-6 months ago so not much credibility for today. I'd like some more opinions on the matter. So... For today, does the idea of running AI agents overnight to do coding tasks make sense? If so, what use cases does it make sense for and what would a sensible setup look like? What are the trade-off and practical costs you may face? submitted by /u/mehow_j [link] [comments]

non coder, 6 months on max, my favorite use of claude is honestly the boring ai content generator stuff

I am not a developer. I run a small training and content business. Have been on Max since february. Everyone in this sub talks about agents and skills and Claude Code. I do not use most of that. My favorite use of claude is the most boring thing imaginable. It is the ai content generator for the work I find tedious. Specifically: 80% of my client emails. I write a 2 sentence brief, claude drafts, I edit, I send. Training material first drafts. I dump notes, claude builds an outline with timing. The 800 word weekly update to my retainer clients. Claude drafts, I revise. Slack messages I have rewritten 3 times in my head. I tell claude what I am trying to say and what I am worried about. It writes the version I would have if I had 20 more minutes. I will not use claude for: my newsletter intro, instagram captions, anything that needs to sound like me to people who know me, anything emotional. I also use google docs ai for the surface polish on long documents claude has drafted. They are different tools doing different work. What I have not yet figured out: a use case where claude is actually replacing my judgment rather than my typing. I think that's correct? The judgment is the work. Curious what other non coders are doing past 6 months.

Streamline your nonprofit purchase requests. Prompt included.

Hello! Are you struggling to create a compliant purchase-request process for your nonprofit? It can be overwhelming to gather all the necessary rules, constraints, and data while ensuring everything complies with funding guidelines. This prompt chain helps you build a structured purchase-request process from scratch. It breaks down the essential steps to extract key information, create an intake form, outline financial routing, and gather feedback for refinement—all tailored to your organization's needs. **Prompt:** VARIABLE DEFINITIONS ORG=Official name of the nonprofit CURRENCY=Currency symbol or code used in financial documents DOC_FORMAT=Preferred final format (e.g., Google Form, Excel, Fillable PDF) ~ You are a senior nonprofit operations analyst. Your task is to extract all rules, constraints, and data needed to design a compliant purchase-request process for ORG. Step 1 – Review Inputs: a. Budget spreadsheets b. Vendor quotes c. Grant restrictions d. Approval-chain email threads e. Past purchase logs Step 2 – From each source, list: • Relevant funding codes or grant IDs • Spending caps or restricted line items • Mandatory approvers and dollar thresholds • Required backup documents • Typical vendors and commodity categories Step 3 – Provide output in a 5-column table: 1. Source Document 2. Key Policy or Data Point 3. Short Description 4. Impact on Purchase Workflow 5. Notes/Exceptions Ask the user to paste or upload summaries of the above documents, then continue when ready. ~ Using the table produced earlier, build the full Purchase Request Intake Form for ORG. 1. Create clearly labeled sections: • Requester Information • Purchase Details (item, qty, unit cost, total cost in CURRENCY) • Request Reason / Program Alignment • Funding Source (budget code, grant ID, allowable amount) • Documentation Checklist (vendor quote, W-9, grant approval, etc.) • Required Approvals (auto-populate names, titles, and thresholds) • Finance Routing Path (sequential steps until disbursement) 2. For each section, list individual fields with field type (text, dropdown, file upload, auto-calc, etc.). 3. Flag any conditional logic (e.g., “If total > $5,000 then require Board Treasurer approval”). 4. Output in an easily copy-pasted table. Example columns: Section | Field Label | Field Type | Required? | Conditional Logic. 5. Tailor labels and instructions to match ORG’s terminology. 6. At the end, present an example of how the form would look in the chosen DOC_FORMAT. ~ Detail the Finance Routing Path extracted from previous steps. 1. Present as numbered steps from submission to payment release. 2. For each step include: Responsible Role, Action Required, SLA (business days), Approval Threshold (if any), and System/Tool used (e.g., email, ERP, DocuSign). 3. Highlight any parallel approvals that can occur simultaneously. 4. Conclude with audit-trail storage location and retention period. ~ Review / Refinement Provide the complete intake form, finance routing path, and underlying policy table to the requester. Ask: • Does the form capture all necessary fields? • Are approval thresholds and funding codes accurate? • Is the routing path practical for everyday use? Incorporate any feedback and deliver the finalized package in DOC_FORMAT. Make sure you update the variables in the first prompt: ORG, CURRENCY, DOC_FORMAT. Here is an example of how to use it: For example, if you're working with a nonprofit called "Help Save The Planet," you might use: ORG=Help Save The Planet CURRENCY=USD DOC_FORMAT=Google Form If you don't want to type each prompt manually, you can run the Agentic Workers, and it will run autonomously in one click. NOTE: this is not required to run the prompt chain Enjoy!

MergeNB: An intuitive merge conflict resolver built for Jupyter notebooks in VS Code [P]

I used to work heavily with Jupyter Notebooks + git + VS Code in a collaborative research setting and found nbdime to be somewhat buggy/a hassle to work with in general. So, in typical side project fashion (relevant xkcd) I've been working on MergeNB quite a bit over the last 6 months or so. It's (currently only) a VS Code extension with a web UI, and has a few cool improvements over other alternatives, which I outlined in the README/docs site. I'd be over the moon if this actually gets used by people, and would love a star if it's interesting. See https://github.com/Avni2000/MergeNB. I've also been working on a static documentation site here: https://avni2000.github.io/MergeNB/docs I'm planning on working on it a lot more over the summer and properly fleshing out a few of the ideas I had (including making it a git mergetool as well as a VS Code extension), so if you'd like to contribute, feel free to raise an issue or shoot me a message/email :) submitted by /u/EnderAvni [link] [comments]

Develop an efficient Client File Audit SOP. Prompt included.

Hello! Are you struggling to create a comprehensive and organized Client File Audit SOP for your medical spa? This prompt chain will help you develop a clear outline and full SOP tailored to your specific medspa operations, ensuring compliance and efficiency in your audit processes. **Prompt:** VARIABLE DEFINITIONS [MEDSPA_NAME]=Official name of the medspa [AUDIT_FREQUENCY]=How often the audit is performed (e.g., monthly, quarterly) [SAMPLE_SIZE_PERCENT]=Percentage of total active client files reviewed each audit cycle ~ You are a healthcare compliance consultant specializing in medical spa operations. Your first task is to develop a clear, organized outline for a Client File Audit SOP for [MEDSPA_NAME]. Follow these instructions: 1. List major SOP sections (e.g., Purpose, Scope, Responsibilities, Definitions, Procedure, Documentation & Record-Keeping, Escalation & Corrective Action, Appendices). 2. Under Procedure, include planned subsections for sampling method, evidence checklist (intake forms, consent documents, appointment records, staff training logs, incident notes), logging of missing items, and escalation triggers. 3. Present the outline as a numbered list with subsection bullets. 4. Ask for confirmation or required adjustments before moving on. Example output style: 1. Purpose 2. Scope • Clients included/excluded 3. Responsibilities • Compliance Officer: … ~ You are still the healthcare compliance consultant. Expand the approved outline into a full Standard Operating Procedure (SOP) for auditing client files at [MEDSPA_NAME]. Steps: 1. Write each SOP section in full sentences and paragraphs; use clear headings. 2. Under "Procedure," detail: a. Sampling methodology: random selection of [SAMPLE_SIZE_PERCENT]% of active files per [AUDIT_FREQUENCY]. b. Evidence checklist specifying required documents (intake forms, consent documents, appointment records, staff training logs linked to service provider, incident notes) and what to verify within each (dates, signatures, completeness). c. Step-by-step audit workflow: preparation, file review, documentation of findings, exit meeting. 3. Under "Documentation & Record-Keeping," include an Audit Log Sheet template table with columns: File ID, Document Type, Evidence Found (Y/N), Notes, Corrective Owner, Due Date, Status. 4. Under "Escalation & Corrective Action," define thresholds for escalation (e.g., >10% critical gaps) and escalation path (Lead Aesthetician → Compliance Officer → Medical Director). 5. Keep language formal and compliance-oriented. 6. Return the complete SOP. ~ Generate two ready-to-use templates referenced in the SOP: 1. Missing Items Tracker (table format with pre-filled column headers). 2. Escalation Decision Tree (flowchart described in text form: IF/THEN steps). Ensure templates align with terminology used in the SOP. ~ Review / Refinement Re-read the entire SOP and templates. Confirm they: 1. Address all required document types. 2. Define sampling, evidence checks, logging, and escalation clearly. 3. Conform to professional tone and formatting. If any criteria are unmet, revise accordingly. Output final refined SOP and templates. Ask the user for any last changes needed.

Storyboard generated from GPT image 2.0

I gave GPT a set of prompts that I found a bit too complicated, and to my surprise, it generated content that matched perfectly. I'm very curious about how GPT Image 2.0 works behind the scenes, and how it can understand and produce high-quality images so quickly. I've included my creation process here; you can view the full image content and try using these prompts directly. https://app.tapnow.ai/tapflow/view/49aa2245 prompt：**PROJECT FILE: HIGH-ALTITUDE ASCENT // PREMIUM HARDSHELL CAMPAIGN** **FORMAT: ARRIRAW 4.5K / KODAK VISION3 50D 5203 EMULATION** **DIRECTOR'S PRE-PRODUCTION VISUAL BOARD** --- ### Top Left Area | Character Lock Zone **[SUBJECT]** 35-year-old male mountain guide/extreme climber. **[WARDROBE]** Top-of-the-line professional jacket (matte rock grey with minimal dark orange taped details), heavy-duty climbing harness. **[VIEWS]** - **Front:** The jacket is fully zipped up, hood pulled up, showcasing a three-dimensional cut and natural drape. - **Side:** Shows ample shoulder and arm movement without bulkiness. - **Back:** Shows the windproof and breathable back panel structure. - **3/4 View:** Dynamic standing pose, holding an ice axe. **[REALISM NOTES]** Realistic human bone structure, slightly asymmetrical. The face has the rough texture of high-altitude red and sun-dried skin, with clearly defined pores and stubble with a frosty look. Rejecting perfect plastic skin, rejecting CG aesthetics. Like a real makeup test photo. --- ### Top Right Area | Expression + Motion Keyframes (EXPRESSION & ACTION) **[EXPRESSIONS]** **Focused:** Slightly furrowed brows, resolute gaze, staring at the rock face above. **Bracing:** Squinting against the strong wind, facial muscles tense. **Breathing:** Lips slightly parted, exhaling real white mist. **[ACTIONS]** **Hood Adjustment:** Pulling the drawstring of the hood with one hand. **Ice Axe Swing:** Arm raised high with force, no pulling sensation under the armpits of the jacket. **Brushing Snow:** Brushing snow off the shoulders, demonstrating the fabric's water-repellent properties. --- ### Upper Middle Area | CAMERA PLAN **[GEAR]** ARRI Alexa Mini LF + Master Prime lens set. **[LENSES]** 24mm (wide-angle environment), 50mm (medium-range tracking shot), 100mm Macro (fabric close-up). **[MOVEMENT PLAN]** - **Shot A (Drone/Crane):** A wide, overhead view, slowly pushing in along a snow-covered ridge. - **Shot B (Handheld):** Shoulder-mounted camera, following the character's movements, with realistic breathing and slight shaking. - **Shot C (Slider):** A close-up panning shot close to the clothing, showing water droplets sliding off. --- ### Central Main Area | Continuous Story Shots (STORYBOARD: 8 PANELS) **[PANEL 01]** - **Shot:** 01 | 24mm | Wide Shot (EWS) | Slow Push-In - **Action:** A tiny figure struggles through a massive natural storm on a snow-covered ridge. - **Detail:** Strong atmospheric perspective; the wind and snow create a realistic fog effect; slight chromatic aberration at the edges of the image. **[PANEL 02]** - **Shot:** 02 | 50mm | Mid Shot | Shoulder-mounted tracking shot - **Action:** A man walks against a blizzard; the strong wind whips against his rain jacket, creating realistic physical wrinkles on the surface, but the overall silhouette remains sturdy. - **Detail:** Noticeable film grain; the snow-capped mountains in the background are slightly out of focus. **[PANEL 03]** - **Shot:** 03 | 100mm Macro | Extreme Close-up (ECU) | Fixed Macro - **Action:** Icy snowmelt hits the shoulders of the rain jacket. - **Detail:** The lotus effect is realistically rendered—water droplets condense and quickly roll off the matte micro-ripstop fabric without penetrating. **[PANEL 04]** - **Shot:** 04 | 85mm | Close-up of face (CU) | Slow motion - **Action:** The man stops and looks up. Real ice crystals cling to his eyelashes, and his breath dissipates at his collar. - **Detail:** Natural skin tone, without excessive blurring; realistic catchlight in his eyes reflects the snow wall ahead. **[PANEL 05]** - **Shot:** 05 | 35mm | Low Angle Full | Handheld, low-angle shot - **Action:** He swings his ice axe into the ice wall, climbing upwards. - **Detail:** Emphasis on showcasing the flexibility of the jacket during vigorous movement; no feeling of restriction; realistic light and shadow highlight the garment's three-dimensional cut. **[PANEL 06]** - **Shot:** 06 | 100mm Macro | Close-up Detail (Insert) | Shallow Depth of Field - **Action:** A heavily gloved hand pulls a waterproof zipper across the chest. - **Detail:** The matte waterproof rubberized finish of the zipper and the clearly visible scratches on the brushed metal zipper pull exude a strong sense of industrial design. **[PANEL 07]** - **Shot:** 07 | 50mm | Over-the-Shoulder Lens (OTS) | Slow Zoom In - **Action:** Over the man's shoulder, we see him finally reaching the summit, sunlight piercing through the clouds and shi

Using Claude ai and Claude code optimally?

So I’m relatively new to using Claude - have some coding experience but by no means anything in terms of building infrastructure. I want to outline how I’ve used Claude so far for my personal projects and see if anyone can help optimise this strategy: I start of outlining a general idea in Claude ai and ask to discuss it and ask me questions about what I want. So for example I built a website where I had a clear idea in my head which I needed to convey to Claude. We talk for a while about it so Claude is on the same page as me as best as I can confirm. Claude then makes a full spec document in pdf from about the project - idea, goal, phases of development etc etc - generally about 25 pages. I review it and let it know any tweaks. I tell Claude I want the document to be a living roadmap i.e. we update it often when I come up with new idea etc. I also tell Claude the work flow is Claude as the architect, Claude code is the builder and I am the go between. I then feed that document to Claude code (as a .md file in git hub) and behind building according to the roadmap in the spec doc. As we go we update the spec doc and re brief Claude code accordingly while it builds the project. Is there more optimal way than this that anyone has used? submitted by /u/DiscombobulatedElk58 [link] [comments]

Funny Claude

Haha! Wth? Opus has big fingers? submitted by /u/Cooked2Antimatter [link] [comments]

Passed Claude CCA-F with 10+ teammates — notes and prep advice

Over the past few weeks, 10+ people on our team have taken and passed the Claude Certified Architect – Foundations (CCA-F) exam. After comparing notes, our main takeaway is: This is not really an API memorization exam. It is much closer to a scenario-based architecture judgment exam. You are not just asked whether you know a Claude feature. You are asked whether you can make reasonable design trade-offs when Claude is used inside real products, agent workflows, developer tools, and automation systems. Some of the recurring questions are more like: Should this task be handled by one agent or multiple sub-agents? Is this tool doing too much? Are the permissions too broad? Is MCP actually needed here, or is it over-engineering? Should this action be automated, or should there be human review? How should structured output be validated? How should long-context workflows be managed reliably? What is the safest next step in a partially automated system? Here are our notes for anyone preparing for the exam. 1. Basic exam structure Based on the official outline and public exam writeups, the exam is: 120 minutes Multiple choice 4 options per question Score range: 100–1000 Passing score: 720 The exam domains are: Agent architecture and orchestration — 27% Tool design and MCP integration — 18% Claude Code configuration and workflows — 20% Prompt engineering and structured output — 20% Context management and reliability — 15% One public writeup also mentioned that there are 6 scenario categories, and the exam randomly selects 4 of them. So this is not a “random facts about Claude” exam. It is much more about reading a realistic scenario and choosing the safest, simplest, most appropriate architecture. 2. The three principles that kept coming up After reviewing the questions we struggled with, we found that many of them came back to three design principles. 1. Least privilege Do not give a tool, agent, or workflow more access than it needs. Examples: If read-only access is enough, do not grant write access. If access to one repository is enough, do not grant access to the whole workspace. If a tool only needs one narrow action, do not expose a broad system-level capability. If an action is high-risk, do not fully automate it without review. A lot of wrong answers look attractive because they are powerful or automated. But they often give the model or tool too much authority. 2. Single responsibility A tool should not do everything. A sub-agent should not become a “general-purpose employee” that retrieves data, makes decisions, modifies files, submits changes, and notifies people all in one step. Many questions test whether you understand where the responsibility should live: Should this be a tool? Should this be agent reasoning? Should this be a human decision? Should this be a separate validation layer? Should this be split into smaller components? If one component is doing too much, be careful. 3. Avoid over-engineering This was probably the biggest pattern. Some answers look sophisticated: Multi-agent orchestration Complex MCP workflows Long-term memory Fully automated tool execution Multi-stage validation pipelines But if the problem is small, narrow, and low-risk, the best answer is often the simplest controlled solution. Our internal summary was: Do not choose the most impressive architecture. Choose the smallest, safest, most controllable one. 3. English reading is a real hidden challenge For non-native English speakers, this may be one of the hardest parts. The questions are often long scenario descriptions. They may include: the current system design the team’s goal existing constraints the risk profile what tools are available what the next step should be The answer choices can also be long. Sometimes one word changes the meaning of the whole option. Words like: automatically always unrestricted without review full access all repositories execute directly can make an option much riskier than it first appears. So our advice is: Practice reading English scenarios directly. Do not rely on translation tools. During the actual proctored exam, you should not expect to use Google Translate, Chrome translation, DeepL, Claude, ChatGPT, or any other external translation tool. For the last few days before the exam, it is worth forcing yourself to read only English material and English practice questions. 4. ProctorFree exam setup The exam is online and uses ProctorFree. The rough flow is: You receive the exam email. You follow the exam link. You download and install ProctorFree. You complete the pre-exam setup. The system checks camera, microphone, network, and screen recording. You start the exam. The session is recorded. After submission, you wait for the upload to complete. Practical setup tips: Use only one monitor. Disconnect external displays. Close unnecessary applications. Clos

Glia – Local-first shared memory layer (SQLite-vec + FTS5 + Offline Knowledge Graph)

Hey everyone, I wanted to share a project I've been working on called Glia. It is a 100% offline, local-first RAG and memory layer designed to connect your AI web chats (Claude, ChatGPT, DeepSeek) with your local developer tools (Claude Code, Cursor, Windsurf) using a unified local database. I wanted something lightweight that did not require pulling heavy Docker containers or subscribing to third-party memory APIs. I settled on a Node.js + SQLite architecture running sqlite-vec (for 768-dim float32 embeddings) alongside SQLite FTS5 for hybrid search, powered completely by local Ollama instances. We just launched a live website that outlines the details and demonstrates the features in action: Website: https://glia-ai.vercel.app/ Codebase: https://github.com/Eshaan-Nair/Glia-AI Technical Stack & Features: Hybrid Search Retrieval: SQLite-vec (using nomic-embed-text locally) + FTS5 keyword prefix matching (porter stemmer). Surgical Sentence-level Trimming: Chunks are sliced into sentences. When a prompt is intercepted, only the exact matching sentences are pulled out of the vector store instead of the whole paragraph. It cuts LLM prompt bloat by ~90-95% in my benchmarks. Knowledge Graph Extraction: An offline task queue uses a local LLM (llama3.1:8b via Ollama) to extract entity triples (subject-relation-object). These are stored in a SQLite facts table (or Neo4j if you run the full Docker compose profile) and fused with the vector retrieval score. HyDE (Hypothetical Document Embeddings): Queries are pre-processed to generate a hypothetical answer, which is embedded together with the original query to bridge semantic gaps. Concurrency: Running SQLite in WAL (Write-Ahead Logging) mode allows the browser extension dashboard and active MCP sessions to read/write concurrently without locking. PII Redaction: Aggressive scrubbing of JWTs, API keys, emails, and IPs in the extension before data is saved. The extension works on Claude.ai, ChatGPT, DeepSeek, Gemini, Grok, and Mistral. The MCP server runs out of the same backend database for your terminal agent or Cursor. You can set it up with a single command: npx glia-ai-setup Glia is completely open-source (MIT). If you like the local-first approach or want to contribute to the SQLite vector pipeline, PRs are very welcome, and a star on GitHub helps the project get discovered! I would appreciate any feedback on the SQLite hybrid search scaling, the scoring fusion algorithm (RAG pipeline details are in RAG_PIPELINE.md), or local graph extraction performance. submitted by /u/Better-Platypus-3420 [link] [comments]

Professor said I used AI

I had completed an assignment, all of which I’ve used AI in help to do them. But I type them up completely on my own. This on particular assignment all he said was that AI was basically flagged, and he would have to give me a zero. I responded with just truth on the fact I used AI for outline and organizing thoughts, and I see now that I leaned too heavily on that outline when writing the paper. Should I have lied? What do you think I should do next? A lot coming up for me academically in the next year and this makes me nervous. I know I’m not in the right, but still looking for general advice. submitted by /u/aspiringphilosopher6 [link] [comments]

The Power of a Full Writers Room, in the Palm of your Hand.

So this project was built exclusively with Claude, Claude Code, and Claude Design. It was built to solve a problem that I have. I'm absolutely horrible at turning a story idea into an outline. I have a LOT of story ideas. Give me a detailed blueprint and I will write the holy hell out of it... But, building that blueprint myself? ABSOLUTELY Hopeless. And I have so many ideas just rotting in a folder because I couldn't get them off the ground. So I built AI-StoryForge. This is not another AI writing tool. It doesn't write a single line of your story. What it does is solve the part that was killing me and probably killing you too! It tracks your information so your plot doesn't contradict itself. It builds psychological profiles for your characters so you can write them like real people, not mechanical puppets, all based on real researched Psychology and Neuroscience. It does live market research against current and past bestsellers. You will know exactly where your idea and story fit in the market before you even write a single word. It maps your story idea and genre selections against genre expectations. It offers you genre conventions to follow so you don't accidentally break rules you don't know exist. Or maybe you do! That's the beauty! Your words. Your voice. Your story. AI-StoryForge just hands you the blueprint to follow. Or not. Your choice. Visit us at www.ai-storyforge.com to see what we offer. submitted by /u/Tartarus1040 [link] [comments]

I thought AI articles could be generated with 2-3 prompts. I ended up building an 11-step workflow.

When I started this project, I honestly thought article generation would be simple: Give Claude a topic Ask for an outline Generate the article Done In reality... the output usually felt generic, repetitive, or structurally weak. So over time the workflow became much more complex. Right now the pipeline I built with Claude Code uses ~11 separate prompts/steps: topic planning search intent analysis outline generation competitor structure analysis section-by-section generation intro/conclusion generation content enhancement internal linking SEO cleanup image generation final formatting/export One thing that improved quality a lot: I stopped treating article generation as a single prompt. Generating sections independently with focused context produced MUCH better results than asking for a full article at once. Another big improvement: I started enriching prompts with external SEO/search data. Now the workflow also analyzes: Google search result structures competitor headings/topics related keyword data search intent patterns I use SEO APIs to feed that data into the prompts before generation. The result feels way less “AI fluffy” compared to my earlier versions. I’ve been testing it on my own websites (one blog for dog owners and website about tattoo) I publish content on two blogs and use this workflow regularly there. I’m actually pretty happy with the results I’m getting from it. OutscoreAgent Still experimenting a lot with workflows/agents, so I’d genuinely love feedback from people here using Claude for similar tasks. There’s a free tier (5 free articles + 14-day trial) if anyone wants to test it. submitted by /u/PlentyButterfly4462 [link] [comments]