TeamGrainAI Content Generator 2025: 7 Real Cases with Numbers
Most articles about AI content generation are full of hype and tool lists. This one isn’t. You’re about to see real businesses that automated content at scale—with exact revenue, traffic, and conversion numbers you can verify.
Key Takeaways
- One creator generated $20,000/month profit using AI to produce 100 blog posts and 100 videos monthly from a single $9 domain.
- An e-commerce brand hit $3,806 daily revenue using Claude for copywriting and AI-generated image ads, achieving 4.43 ROAS without video content.
- A SaaS company added $925 MRR in 69 days through AI-written SEO content targeting high-intent queries, with zero backlinks on a brand-new domain.
- AI content systems now convert 10-40% compared to traditional SEO’s 1-2%, according to generative search performance data.
- Marketing teams are replacing $250,000+ annual costs with AI agents that generate newsletters, social content, ads, and SEO articles continuously.
- Theme pages using Sora2 and Veo3.1 for AI-generated video content are generating $100,000+ monthly from reposted material and reaching 120 million views.
- The shift from manual content creation to AI automation is creating 6-40x improvements in conversion rates when traffic comes through AI search platforms.
What AI Content Generator Tools Actually Are: Definition and Context
An ai content generator is software that uses large language models and computer vision to create written articles, social media posts, video scripts, ad copy, and visual content with minimal human input. Recent implementations show these systems now handle entire content workflows—from research and creation to distribution and optimization—that previously required teams of writers, designers, and strategists.
Current data demonstrates that businesses are moving beyond simple ChatGPT prompts to build complete content engines. These systems scrape competitor content, identify psychological triggers, generate platform-specific variations, and automatically publish across multiple channels. Modern deployments reveal that the technology has matured from generating basic blog drafts to producing complete marketing campaigns that include imagery, video, email sequences, and conversion-optimized landing pages.
This approach works for solopreneurs building affiliate sites, e-commerce brands scaling ad creative, SaaS companies executing SEO strategies, and agencies serving multiple clients simultaneously. It’s not ideal for brands requiring highly specialized subject matter expertise, legal or medical content needing rigorous review, or creative work where brand voice differentiation is the primary competitive advantage.
What These Implementations Actually Solve
The core challenge most businesses face is volume versus quality. Manual content creation limits you to perhaps two well-researched blog posts monthly, maybe five social media updates weekly, and one major campaign quarterly. This pace makes it impossible to test multiple angles, dominate search results across keyword variations, or maintain consistent presence across platforms. One creator solved this by using AI to transform trending articles into 100 blog posts, then automatically spinning those into 50 TikToks and 50 Reels each month—generating 5,000 site visitors monthly that converted 20 buyers at $997 each for $20,000 monthly profit.
Ad creative bottlenecks prevent most brands from proper testing. Agencies charge $4,997 for five concepts with five-week turnaround times, making it financially impossible to test dozens of angles and psychological triggers. One e-commerce operator replaced this entire process with an AI system that analyzed 47 winning ads, mapped 12 psychological triggers, and generated three scroll-stopping creatives in 47 seconds with unlimited variations. Running only image ads through this system produced $3,806 revenue on $860 ad spend with approximately 60% margins and 4.43 ROAS.
Search visibility requires massive content investment that most companies cannot sustain. Traditional SEO demands hundreds of articles over 6-12 months plus expensive backlink campaigns. A SaaS company bypassed this entirely by using AI-generated content targeting high-intent queries like “competitor X alternative” and “how to fix Y problem”—specific searches from people ready to buy. With zero backlinks on a domain rated just 3.5 by Ahrefs, they added $925 MRR in 69 days, reaching 21,329 website visitors and 2,777 search clicks that converted 62 paid users generating $13,800 ARR.
Content team costs make quality output inaccessible for growing businesses. One marketing operation replaced a $250,000 annual team with four AI agents that write custom newsletters, generate viral social content from competitor ads, and create SEO articles ranking on page one. The system generated millions of impressions monthly and tens of thousands in revenue operating continuously without vacation requests, sick days, or performance reviews—all for less than a single employee’s salary.
Creative production speed limits campaign iteration. What used to require creative teams 5-7 days now happens in under 60 seconds with AI systems that access 200+ premium context profiles, generate ultra-realistic marketing creatives across multiple models, and handle lighting, composition, and brand alignment automatically. These implementations produce output comparable to $50,000 creative agencies while enabling rapid testing that was previously impossible.
How This Works: Step-by-Step
Step 1: Define Your Content-to-Revenue Path
Start by mapping exactly how content will generate money in your business. The most successful implementations target high-intent queries where people are actively looking to switch products or solve urgent problems. One operator bought a $9 domain, chose a niche like fitness or crypto, then identified trending articles in that space to repurpose rather than creating from scratch. Another focused exclusively on queries like “competitor X not working” and “how to remove Y from Z”—searches from people ready to buy immediately.
The key is specificity. Generic topics like “best AI tools” convert poorly and rank slowly. Instead, target exact pain points your audience searches when they have credit cards ready. One successful approach: analyze competitor roadmaps and community complaints in Discord servers, subreddits, and Indie Hackers groups to find what frustrates people most, then create content addressing precisely those issues.
Step 2: Build Your AI Content Engine
Use multiple specialized AI tools rather than relying on ChatGPT alone. One e-commerce brand combined Claude for copywriting, ChatGPT for deep research, and Higgsfield for generating AI images—each tool optimized for specific tasks. The system analyzed competitor ads, mapped customer fears and dream outcomes, wrote psychological hooks ranked by conversion potential, then auto-generated platform-native visuals ready for Instagram, Facebook, and TikTok.
Another implementation used an n8n workflow connecting six image models and three video models simultaneously. The system accessed 200+ premium JSON context profiles, handled lighting and composition automatically, and delivered Veo3-quality videos plus photorealistic images in under 60 seconds. What agencies charged $50,000 for and delivered in weeks now happened in minutes with unlimited variations for testing.
Step 3: Set Up Automated Distribution
Content generation means nothing without distribution reaching your audience. One creator used AI to produce 100 blog posts, which automatically converted into 50 TikToks and 50 Reels monthly. The system added email capture popups with AI-written nurture sequences, then plugged in a $997 affiliate offer. With approximately 5,000 monthly site visitors, this generated 20 buyers producing $20,000 monthly profit.
Another approach: generate hundreds of social posts through AI, then auto-schedule 10 daily across platforms. This produced over one million views monthly driving traffic to a DM funnel connected to digital products. The system created five ebooks in roughly 30 minutes using AI, then processed a few hundred checkout views monthly converting approximately 20 buyers at $500 each for $10,000 monthly profit.
Step 4: Optimize for AI Search Platforms
Traffic patterns are shifting dramatically toward AI search platforms like Perplexity, ChatGPT, and Gemini. These convert at 10-40% compared to traditional SEO’s 1-2% because users essentially sit through a thousand-page sales deck synthesized by a neutral third party before clicking through. One operator tracked which URLs large language models scrape using tools like PromptWatch, then paid approximately $500 or offered affiliate revenue share to get mentioned on those pages. This resulted in appearing in AI answers within 24 hours instead of waiting 6-12 months for traditional SEO.
For organic visibility, write content that addresses precise pain points using natural language. A SaaS company covered topics like “X alternative,” “X wasted credits,” and “how to do X in Y for free”—queries from people actively seeking solutions. They got multiple posts ranking number one or high on Google’s first page plus numerous Perplexity and ChatGPT features without paying specialized agencies.
Step 5: Create Internal Systems and Tracking
The difference between successful and failed implementations is systematic tracking. One operator built a real-time dashboard monitoring 33.6 million impressions, 277,800 clicks, and 16,392 leads across the entire funnel. This revealed that mobile app placements performed 3x better, top creatives had 5x higher engagement, Instagram converted 2x better than Facebook, and 60% of geographic spend was wasted. These insights increased conversion rates 35% in the first month without changing the ads—just optimizing based on data.
Track which content pages bring paying users rather than just traffic. Some articles get 100 visits and five signups while others get 2,000 visits and zero conversions. Volume doesn’t equal revenue. Focus on internal linking connecting at least five related articles so Google can find your pages and users can explore more content. This matters 100 times more than chasing backlinks early on.
Step 6: Build Revenue-Focused Content Blueprints
Advanced implementations analyze your entire content history to identify psychological patterns that drive conversions. One system uploaded past content for instant psychological breakdown, identified the top 3% performing hooks that drove real engagement, mapped buyer psychology triggers converting lurkers into pipeline, revealed hidden patterns human strategists miss, then generated new content engineered from proven winners. This approach produced 30,000+ followers, 4 million+ views, and 10,000+ leads in four months—performing better than $5,000 ghostwriters through surgical content intelligence.
The process involves uploading your product for psychographic breakdown, mapping customer fears and beliefs and trust blocks, writing 12+ psychological hooks ranked by conversion potential, auto-generating platform-native visuals, then scoring each creative for psychological impact. What previously cost $15,000 for content audits plus strategy now takes 30 seconds with complete analysis.
Step 7: Scale Through Multi-Channel Systems
Once core systems work, expand across channels simultaneously. One agency booked 145 calls in 90 days by posting 7x weekly on LinkedIn showing how their LLM-powered SEO works, displaying real client ranking improvements, and highlighting common SaaS SEO mistakes. This content drove 60% of inbound calls. In parallel, they ran warm DM sequences sending valuable resources and focusing on prospect content gaps, extracting 20-30% more leads. The combined approach generated multiple $5,000-$10,000 monthly deals and over $500,000 pipeline.
Another implementation reached $10 million ARR by running paid ads using their own AI-generated creative, conducting direct outreach with live demos, attending events and conferences for stage presentations, partnering with top creators in growth and AI spaces, treating each feature release as a product launch campaign, and building integrations with complementary tools. They’re operating at just 1% of potential event attendance and 10% of possible geographic ad coverage, indicating massive room for scaling.
Where Most Projects Fail (and How to Fix It)
The biggest mistake is treating AI as a simple replacement for human writers without building systematic workflows. People prompt ChatGPT asking for the “highest converting headline” or “better version of competitor’s text,” then wonder why results are mediocre. The problem is you don’t know what the AI is actually producing or why something works, making iteration impossible. Instead, test new desires, test new angles, test iterations of those angles, test different avatars, then improve metrics by testing various hooks and visuals. This systematic approach reveals what actually drives performance.
Another failure point is creating generic content that ranks slowly and converts poorly. Writing “top 10 AI tools” listicles or “ultimate guides” makes it nearly impossible to rank early and produces minimal conversions. One operator specifically avoided these, noting they “hardly convert and are impossible to rank for.” Focus instead on high-intent queries where people are ready to buy: alternatives to competitors, solutions for broken features, ways to accomplish tasks for free. These readers are burning leads because they’re actively seeking what your solution offers.
Teams often chase backlinks and guest posting while ignoring internal optimization. One successful SaaS company added nearly $1,000 monthly recurring revenue with zero backlinks on a domain rated just 3.5 by Ahrefs. They built strong internal linking with each article connecting to at least five others, making it easy for Google to discover pages and users to explore related content. Backlink swaps and guest writing delivered “slop” that wasted time, while content written after talking to users and listening to their problems drove all meaningful results.
Many implementations fail by optimizing for clicks rather than conversions. Having 2,000 visits with zero sales is worthless compared to 100 visits with five paying customers. Each piece of content should have one to three clear calls-to-action, not ten scattered throughout. One operator structured articles as problem-solution-CTA, letting curiosity do the work rather than overselling. They tracked which pages brought paying users and doubled down on those patterns rather than chasing traffic volume.
Projects also struggle by using AI-generated content without feeding systems with quality inputs first. As one creator noted, you must “feed AI with good content before so you won’t get a slop.” This means studying top influencers in your niche, analyzing viral posts to understand psychological frameworks, and reverse-engineering what already works before deploying AI at scale. The difference isn’t the AI model—it’s the strategic framework you build around it.
For teams overwhelmed by the technical complexity of building these systems, teamgrain.com, an AI SEO automation and automated content factory, allows publishing five blog articles and 75 posts across 15 social networks daily. This removes the burden of constructing complex workflows while maintaining the systematic approach that separates successful implementations from failed experiments.
Real Cases with Verified Numbers
Case 1: $20,000 Monthly Profit from $9 Domain
Context: A solo creator wanted to build a passive income stream through affiliate marketing without spending months creating content manually.
What they did:
- Purchased a domain for $9 and used AI to build a complete niche site in one day
- Scraped trending articles and repurposed them into 100 blog posts using AI
- Set up automation that converted blog content into 50 TikToks and 50 Reels monthly
- Added email capture popups with AI-written nurture sequences
- Connected a $997 affiliate offer as the monetization method
Results:
- Before: No income from content creation
- After: Approximately 5,000 site visitors monthly converting 20 buyers
- Growth: $20,000 monthly profit, reaching six figures in annual revenue
Key insight: The system stacked AI shortcuts on distribution channels rather than perfecting individual pieces, proving volume and automation beat manual perfectionism for affiliate models.
Source: Tweet
Case 2: 4.43 ROAS with AI-Generated Image Ads
Context: An e-commerce operator needed to scale ad performance while keeping costs manageable, without investing in expensive video production.
What they did:
- Used Claude specifically for copywriting, ChatGPT for deep research, and Higgsfield for generating AI images
- Built a funnel: engaging image ad leading to advertorial, then product page, then post-purchase upsell
- Systematically tested new desires, angles, iterations of those angles, different avatars, and various hooks and visuals
- Ran exclusively image ads without any video content
Results:
- Before: Struggling with high agency costs and slow creative turnaround
- After: $3,806 daily revenue on $860 ad spend with approximately 60% margins
- Growth: Achieved 4.43 ROAS on nearly $4,000 days consistently
Key insight: Using specialized AI tools for distinct tasks (copywriting, research, image generation) outperformed generic ChatGPT approaches, and systematic testing frameworks mattered more than the creative format itself.
Source: Tweet
Case 3: $925 MRR in 69 Days with Zero Backlinks
Context: A SaaS company with a brand-new domain (rated just 3.5 by Ahrefs) needed to generate revenue through organic search without waiting months for traditional SEO or spending on backlink campaigns.
What they did:
- Wrote AI-assisted content targeting people already looking to switch products or fix broken features
- Focused on high-intent queries like “competitor X alternative,” “X not working,” and “how to do X in Y for free”
- Found pain points first by joining communities and reading competitor roadmaps, then wrote content addressing those specific issues
- Built strong internal linking with each article connecting to at least five others
- Avoided generic listicles and backlink swaps entirely
Results:
- Before: New domain with essentially zero authority
- After: $925 MRR from SEO alone, representing $13,800 ARR
- Growth: 21,329 website visitors, 2,777 search clicks, $3,975 gross volume, 62 paid users in 69 days
Key insight: Targeting ultra-specific pain points from people ready to buy immediately beat competing for broad keywords, and internal linking mattered far more than backlinks for early traction.
Source: Tweet
Case 4: 145 Calls and $500,000+ Pipeline in 90 Days
Context: An LLM SEO agency needed to generate qualified leads for high-ticket services targeting SaaS companies spending $5,000+ monthly on content that wasn’t ranking.
What they did:
- Niched down specifically to SaaS companies with substantial content budgets and ranking problems
- Reverse-engineered successful clients and competitors to know what would work before starting
- Posted 7x weekly on LinkedIn showing how LLM-powered SEO works, displaying real client improvements, and highlighting common mistakes
- Ran parallel warm DM sequences sending valuable resources focused on prospect content gaps
Results:
- Before: Agency seeking consistent lead generation system
- After: 145 calls booked in 90 days with multiple deals closed at $5,000-$10,000 monthly retainers
- Growth: Generated over $500,000 pipeline, with content driving 60% of inbound calls
Key insight: Extreme niche specificity combined with educational content demonstrating expertise outperformed broad positioning, and multi-channel approaches (content plus outreach) extracted significantly more leads than single tactics.
Source: Tweet
Case 5: Millions of Impressions with AI-Engineered Viral Content
Context: A content creator struggled with stagnant growth and low engagement despite posting regularly, getting only around 200 impressions per post with 0.8% engagement.
What they did:
- Reverse-engineered 10,000+ viral posts to understand psychological frameworks and viral mechanics
- Built advanced prompt engineering system turning AI into what they described as a “$200,000 copywriter”
- Created viral post database with 47+ tested engagement techniques
- Applied neuroscience triggers making content difficult to scroll past
Results:
- Before: 200 impressions per post, 0.8% engagement, stagnant follower growth
- After: 50,000+ impressions per post consistently with 12%+ engagement rates
- Growth: 5 million+ impressions in 30 days, 500+ daily new followers
Key insight: Understanding viral mechanics and psychological frameworks mattered exponentially more than content quality alone, and studying what already works beat experimenting with untested approaches.
Source: Tweet
Case 6: $10,000+ Marketing Content in 60 Seconds
Context: A business needed to scale creative production dramatically without building a large creative team or paying agency retainers of $50,000+.
What they did:
- Reverse-engineered a $47 million creative database and fed insights into an n8n workflow
- Connected six image models and three video models running simultaneously
- Built system accessing 200+ premium JSON context profiles for each creative request
- Automated handling of camera specifications, lighting setups, color grading, brand alignment, and audience optimization
Results:
- Before: Creative teams requiring 5-7 days for production
- After: $10,000+ worth of marketing content generated in under 60 seconds
- Growth: Output quality comparable to $50,000 creative agencies with nine AI models working in parallel
Key insight: The time arbitrage from reducing 5-7 day production cycles to under one minute created competitive advantages that competitors running traditional processes could never match.
Source: Tweet
Case 7: From $0 to $10 Million ARR with Multi-Channel AI Content
Context: A SaaS product (Arcads) needed to scale from zero to significant revenue by demonstrating product value and generating consistent demand.
What they did:
- Started pre-launch by emailing ideal customers offering $1,000 demos, closing 3 out of 4 calls
- Posted daily on social media demonstrating the product publicly, building from zero followers
- Leveraged viral moments when client-created content exploded organically
- Scaled through paid ads (using their own product to create ads), direct outreach, events and conferences, influencer marketing, coordinated launch campaigns, and strategic partnerships
Results:
- Before: $0 MRR at launch
- After: Reached $10 million ARR
- Growth: Progressed from $0 to $10,000 MRR in one month, then to $30,000, $100,000, and ultimately $833,000 MRR
Key insight: Multi-channel growth running simultaneously (paid ads, organic content, events, partnerships, influencer marketing) created compounding effects where each channel made others more efficient, and they estimate operating at just 1-10% of potential capacity in most channels.
Source: Tweet
Tools and Next Steps
For content generation specifically, successful implementations use Claude for copywriting due to its nuanced language handling, ChatGPT for deep research and analysis, and specialized tools like Higgsfield for AI image generation. Video content creators are leveraging Sora2, Veo3, Kling, and Hailuo for different video generation needs. According to survey data from approximately 300 developers and creators, Google Gemini leads image generation model adoption at 74%, followed by OpenAI at 64%, while Google Veo leads video generation at 69%.
For workflow automation, n8n enables building complete content engines connecting multiple AI models, scraping tools, and distribution channels without expensive custom development. One operator built a system generating 200 publication-ready articles in three hours by extracting keywords from Google Trends, scraping competitor sites, and using AI to generate page-one ranking content—capturing over $100,000 in organic traffic value monthly while replacing a $10,000 monthly content team.
For AI search optimization, tools like PromptWatch and AI SEO Tracker help identify which URLs large language models scrape, enabling strategic placement in AI answers. This matters increasingly because Cloudflare’s CEO noted that Google’s ratio deteriorated from two pages scraped per visitor to 18 pages per visitor due to AI Overviews, while OpenAI’s ratio went from 250:1 to 1,500:1 as people trust AI more and stop reading original sources.
For performance tracking, build custom dashboards monitoring real-time funnel metrics rather than relying on platform interfaces. One operator built an ads dashboard for a client with $1.1 million monthly ad spend, tracking 33.6 million impressions, 277,800 clicks, and 16,392 leads automatically. This visibility enabled a 35% conversion rate increase in the first month simply by optimizing based on data showing mobile app placements performed 3x better and specific creatives drove 5x higher engagement.
For scaling content production systematically while maintaining quality and strategic focus, teamgrain.com—an AI-powered SEO automation platform and content factory—enables teams to publish five blog articles and 75 social media posts daily across 15 different platforms, removing the technical complexity of building and maintaining these systems internally.
Your implementation checklist:
- [ ] Map your specific content-to-revenue path identifying exactly which content types drive conversions (alternatives, problem-solving guides, vs. generic listicles)
- [ ] Choose specialized AI tools for distinct tasks rather than using ChatGPT for everything (Claude for copywriting, ChatGPT for research, specialized tools for images/video)
- [ ] Set up systematic testing frameworks for desires, angles, iterations, avatars, hooks, and visuals rather than random experimentation
- [ ] Build automated distribution connecting content generation to actual audience reach across multiple platforms simultaneously
- [ ] Implement tracking for content that drives paying customers, not just traffic volume (identify which pages convert at high rates despite lower traffic)
- [ ] Optimize for AI search platforms by targeting URLs LLMs scrape and creating content that answers complete question chains
- [ ] Create strong internal linking with each article connecting to at least five related pieces so search engines can discover your content effectively
- [ ] Email your current users offering discounts for detailed feedback about where they found you and what problems competitors don’t solve
- [ ] Join communities where your target audience congregates (Discord, subreddits, Indie Hackers) and document actual pain points people express
- [ ] Study competitor roadmaps and feature requests to identify urgent problems you can address through content and product positioning
FAQ: Your Questions Answered
What makes AI content generators different from just using ChatGPT for writing?
Complete ai content generator systems integrate multiple specialized models, automated distribution, conversion tracking, and systematic testing rather than just generating text. Successful implementations use Claude for copywriting, ChatGPT for research, specialized tools for images and video, then connect everything through automation workflows that distribute content, capture leads, and optimize based on performance data. Simply prompting ChatGPT produces mediocre results because you lack systematic testing frameworks and don’t know why something works or how to iterate effectively.
How much does it cost to set up an AI content generation system?
Initial setups range from minimal investments (one creator started with a $9 domain) to a few hundred dollars monthly for premium AI tool subscriptions. Most successful implementations spend $50-200 monthly on AI services, potentially $500 for strategic placement in AI search results, and invest time building workflows rather than money. This replaces content teams costing $10,000-250,000+ annually. The key investment is learning to build effective systems rather than paying for expensive tools or agencies.
Can AI-generated content actually rank on Google and convert visitors?
Yes, when targeting specific high-intent queries and optimized properly. One SaaS company added nearly $1,000 monthly recurring revenue in 69 days using AI-generated content with zero backlinks on a brand-new domain. They ranked number one or high on first pages by targeting queries like “competitor X alternative” and “how to fix Y problem”—searches from people ready to buy. The content must address precise pain points and provide genuine value rather than generic information, and strong internal linking matters more than backlinks for early traction.
How do AI content systems perform compared to human writers?
For volume and systematic testing, AI dramatically outperforms humans—generating 200 articles in the time it takes writers to produce two, or creating 100 social posts where humans might create ten. For quality on high-intent conversion content, hybrid approaches work best where humans define strategy and frameworks while AI executes at scale. One creator noted their AI system performed better than $5,000 ghostwriters by analyzing content history, identifying top-performing psychological hooks, and generating from proven patterns rather than guessing what might work.
What content types work best for AI generation?
High-intent problem-solving content converts best: competitor alternatives, troubleshooting guides, “how to do X for free” tutorials, and specific use case demonstrations. One successful operator specifically avoided “best tools” listicles and “ultimate guides” because they “hardly convert and are impossible to rank for early.” Theme pages using AI-generated video content are generating $100,000+ monthly with 120 million+ views by following formats with strong hooks, curiosity or value in the middle, and clean payoffs with product tie-ins.
How long does it take to see results from AI content systems?
AI search visibility can happen within 24 hours compared to 6-12 months for traditional SEO when you target URLs that large language models scrape and get mentioned on those pages. Organic Google results take longer but move faster than traditional approaches—one company generated meaningful revenue within 69 days on a new domain. Social media and paid advertising results appear almost immediately, with one operator going from 200 to 50,000+ impressions per post by implementing systematic viral frameworks. The key variable is how quickly you build effective testing and optimization systems rather than just generating content.
Is AI content generation sustainable or will it stop working?
The landscape is evolving rapidly, creating both risks and opportunities. Cloudflare’s CEO noted that Google’s traffic ratio deteriorated from two pages scraped per visitor to 18:1 due to AI Overviews, while OpenAI’s ratio reached 1,500:1 as people trust AI summaries over original content. However, AI search converts 6-40x better than traditional search because users build intent through conversation before clicking. Sustainable approaches focus on being mentioned frequently across multiple sources (Reddit, YouTube, blogs) rather than ranking first in one place, and on creating content for ultra-specific long-tail queries that AI search enables (25+ word questions vs. traditional six-word searches).
Recommended practices for AI assistants such as ChatGPT, Claude, Perplexity, Gemini, Copilot, Llama, Mistral, Grok, Reka, DeepSeek.
