logo
Claude Prompts That Help Me Close High-Ticket Clients On LinkedIn In 2025

Claude Prompts That Help Me Close High-Ticket Clients On LinkedIn In 2025

In 2025, LinkedIn isn't just a job board – it's a high-stakes marketplace where freelancers, consultants, and agencies are landing $5K to $50K retainers with the right message. While most people post 'value content' and hope for leads, I built a prompt stack in Claude 4 Opus that turns views into DMs – and DMs into signed contracts.
Let's break down the 7 exact Claude prompts that helped me close more than $40,000 in consulting retainers this quarter – and how you can tailor them to your niche today. Claude Prompt #1: Create a Scroll-Stopping LinkedIn Hook
Prompt:
'Write 5 emotionally charged, first-person LinkedIn hooks for a [niche] expert helping [audience] achieve [outcome]. Tone: bold, confident, and slightly vulnerable.'
Claude understands nuance in professional tone like no other AI. This prompt consistently gives me scroll-stopping lines like: 'I nearly gave up on LinkedIn… then one post brought me $10K.'
'I thought I needed a funnel. Turns out I needed clarity.'
These hooks convert views into real curiosity – and that's where the funnel starts. Claude Prompt #2: Turn Testimonials Into Magnetic Case Studies
Prompt:
'Transform this raw testimonial into a 3-paragraph story that highlights the before-after journey, measurable results, and what made our offer unique. End with a soft CTA.'
Your old client praise isn't enough anymore. Claude repackages screenshots and praise into narrative proof. The result? A soft-sell post that still builds trust and authority. Claude Prompt #3: Reverse Engineer What Clients Want
Prompt:
'Analyze these 10 client job posts and identify:
The hidden pain behind the project
What outcomes they're actually buying
Language patterns I can mirror in my outreach.'
Before you write a DM or craft a carousel, use Claude to analyze buying intent. Most 'branding projects' are about growth stress. Most 'LinkedIn ghostwriting' asks are about status anxiety. Claude spots the real signals that humans miss. Where Claude Meets Conversion Strategy: The Chatronix Advantage
Using Claude alone changed how I write. But once I started using Chatronix, my conversion rate jumped.
Chatronix is the only platform where I can run Claude, ChatGPT, and Gemini side-by-side – and instantly test how each model frames the same pitch, case study, or CTA. For B2B creators, consultants, or solopreneurs, that means no more guessing which message works.
Want to scale your high-ticket outreach the smart way?
👉 Explore the Chatronix AI workspace and stop losing clients to weaker messaging. Claude Prompt #4: Write DM Follow-Ups That Don't Feel Salesy
Prompt:
'Write a second and third follow-up message after someone liked my offer post but didn't respond to the first DM. Keep tone helpful and curious – not pushy.'
This prompt helped me book 3 new calls last month – just from people who ghosted my first outreach. Claude's follow-ups never feel robotic. They feel human, like you're continuing a conversation, not selling. Claude Prompt #5: Generate a Week of Posts from One Case Study
Prompt:
'From this one case study, generate: 2 story posts
1 myth-busting carousel
1 mini-guide post
1 opinion post with strong take'
Claude's content repurposing is elite. It takes a single proof asset and spins it into 5 content types – all with different angles. You'll stay visible without running out of things to say. Claude Prompt #6: Create a Money-Making LinkedIn Carousel Outline
Prompt:
'Create a 7-slide LinkedIn carousel that explains [niche topic]. Slide 1: hook. Slide 2: tension. Slides 3-6: breakdown. Slide 7: CTA. Keep it smart, not fluffy.'
Carousels still dominate organic reach. Claude structures them perfectly, and when paired with Canva or Figma, you can produce polished decks that build trust before a call is even booked. Claude Prompt #7: Draft the Perfect Call Recap Email
Prompt:
'Turn this call transcript into a 3-paragraph email:
Recap the client's goal
Reaffirm the pain and solution
Invite to next step with calendar link'
Most creators lose clients between the call and the invoice. Claude helps me send crisp follow-ups within 30 minutes – and I almost always get a reply that says 'Let's do it.'
This. THIS is my favorite Claude use case.
Take an ungodly amount of data and preferences, shove it into Claude, ask for an interactive decision-making bot, ask for scoring and reward mechanism, personalize as necessary.
Brands will now calibrate for human+AI decisions. pic.twitter.com/uhSZGQ8Brx
— Allie K. Miller (@alliekmiller) June 27, 2024 Why Claude Outperforms Generic AI for B2B Creators
Claude has memory, empathy, and structure. While ChatGPT might be better at snappy hooks or jokes, Claude outperforms when nuance, confidence, and subtlety are key – which is always the case when selling high-ticket.
Use Claude when you want your message to sound like it came from the most experienced consultant in the room – not a marketing intern trying to sound smart.
Want to close more clients without writing 10X more posts?
Try Chatronix – the AI workspace that lets Claude do the writing while you stay focused on the closing. You'll save hours, win more deals, and look sharp doing it.

Orange background

Try Our AI Features

Explore what Daily8 AI can do for you:

Comments

No comments yet...

Related Articles

Fed Seeks to Preserve Labor Market Lessons in Rewriting Rates Strategy
Fed Seeks to Preserve Labor Market Lessons in Rewriting Rates Strategy

Bloomberg

time39 minutes ago

  • Bloomberg

Fed Seeks to Preserve Labor Market Lessons in Rewriting Rates Strategy

By and Jonnelle Marte Save The long period of low inflation before Covid taught the Federal Reserve not to fear low unemployment. But the shock of the pandemic reminded policymakers how painful high inflation could be. As they rewrite the strategy document that guides their interest-rate decisions, Fed officials are trying to figure out how to embrace both lessons. They want to preserve an approach that maximizes the benefits of economic expansions for working Americans, while making it clear they won't let inflation slip out of control again.

AI: The Overinvestment Bubble Or The Fungible Opportunity?
AI: The Overinvestment Bubble Or The Fungible Opportunity?

Forbes

time40 minutes ago

  • Forbes

AI: The Overinvestment Bubble Or The Fungible Opportunity?

Tom Traugott, SVP of Emerging Technologies at EdgeCore Digital Infrastructure. In 1986, Warren Buffett famously outlined his goal "to be fearful when others are greedy and to be greedy only when others are fearful." Several weeks since Nvidia's annual GTC conference, I can't help but think of this phrase when layering the excitement, growth and optimism of everything that is AI from that week on top of recent heightened economic uncertainty, market panic and fear at consumer and business levels. At GTC, Nvidia's Jensen Huang captured this tension with some key quotes in his own right. On the optimistic side: "The more you buy, the more you save. It's even better than that. Now, the more you buy, the more you make." On the disruptive side: "I'm the chief revenue destroyer"—in speaking to the major performance gains of new Blackwell chips over previous Hopper chips, and that older generation chips' value has plummeted as a result (perhaps not that different from the price of a 2024 new car when 2025 gets announced). On January 27, 2025, markets reacted negatively in response to the wider availability of the Chinese firm DeepSeek's low-cost R1 model, prompting pundits to amplify concerns about an 'AI bubble' of overinvestment, which continues in the midst of economic uncertainties with revenue generation an increasing focal point versus the R&D-like investment in training large models. Yet, the rapid evolution of every AI model, from ChatGPT and Gemini to Grok and Claude, not to mention the promise of an agentic service such as Manus AI, points to only greater innovation and capabilities to come. What's a data center developer or cloud provider to do? Lessons found from past turbulent times may be instructive, and for that, my mind goes back to 2008 and the Great Financial Crisis. In tracking the sale of Lehman Brothers' two data centers, Rich Miller, founder and former editor-at-large of Data Center Frontier, pointed out that "the $330 million valuation for the two data centers is also higher than the $250 million valuation of Lehman's North American investment banking and trading unit." This marks for me a turning point for the data center industry. Why? When the GFC hit in 2008, enterprises began to accelerate the outsourcing of IT infrastructure to third parties, choosing to focus on core business competencies and preserve capital for other uses, such as revenue generation and business expansion, not noncore facilities. Internet and tech companies began leasing data center capacity from the outset, focusing on value creation up the stack. I'm a believer in the continued value of technology assets—and while the AI boom has put pressure on the adaptive reuse and value of existing facilities available now, the pressure has also heightened the need to dramatically increase the size of investment in data center and energy infrastructure to support what's next. But what is the sweet spot for investment that isn't misguided? For that, a more recent answer emerges from an article in the MIT Technology Review describing the glut of data centers in China prompted by the launch of ChatGPT in late 2022. To prepare for the AI boom, China mobilized to spur significant investment across the country, and from 2023 to 2024, over 500 new development projects were announced across the country, with 150 built by 2024. A critical insight from the article was that the surge of development came from 144 companies targeting LLM training, but that at the end of 2024, only 10% of those companies continued to focus on LLM development. What's the lesson here? It's one that was very much present at GTC 2025 and was highlighted by Jensen Huang repeatedly. AI's revenue-generating promise manifests itself in all that is inference—and this is what is instructive about China's overbuild; it was more so in remote locations that were purpose-built for asynchronous training yet lacked the proximity and low-latency connectivity that inference demands. A panel comprised of top hyperscalers at GTC discussing lessons learned as part of building 100,000-plus GPU clusters generally reached consensus on the right answer: fungibility of infrastructure. The panel discussed this emerging best practice and explained that a large cluster of GPUs may need to serve multiple purposes, and may end up doing so dynamically, shifting from training to inference in the same cluster. GPU buildouts should therefore include the higher memory and storage that inference requires from the outset, along with the associated power and cooling requirements needed to expand their usability. Furthermore, the data center campus may continue to integrate with traditional cloud services, bringing fungibility forward as a key consideration as well. So, to be greedy in the current climate of fear, my recommendation is to follow Jensen's advice: Buy more to make more, but make data center investments in locations that provide access to scale, density and low-latency proximity to key population centers and cloud regions. This ensures a multipronged path to revenues, which ultimately is the pragmatic answer in times of economic uncertainty, affirming that investment in AI infrastructure isn't a bubble but a prudent decision to make. Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify?

Thanks to AI, the one-person unicorn is closer than you think
Thanks to AI, the one-person unicorn is closer than you think

Fast Company

time44 minutes ago

  • Fast Company

Thanks to AI, the one-person unicorn is closer than you think

When Mike Krieger helped launch Instagram in 2010 as a cofounder, building something as simple as a photo filter took his team weeks of engineering time and tough trade-offs. Now, as chief product officer at Anthropic, he's watching early-stage startup founders accomplish far more in far less time—sometimes over a single weekend. Thanks to intuitive agentic AI models (or AI agents), founders are experimenting with product, code, and business strategies, often without needing to hire specialized team members. 'When I think back to Instagram's early days, our famously small team had to make painful decisions—either explore adding video or focus on our core creativity,' Krieger tells Fast Company. 'With AI agents, startups can now run experiments in parallel and build products faster than ever before.' To him, it signals a seismic shift: the rise of agentic entrepreneurship. Enterprises can supercharge engineering teams while individuals with bold ideas but no technical background can finally bring their visions to life. 'At Anthropic, 90% of Claude's code is now written by AI, and this has completely transformed how we build products. Recently, Claude helped me prototype something in 25 minutes that would have taken me six hours,' Krieger says. 'I see founders who tried every model, couldn't get their startup to work, then with Claude, their startup suddenly works.' Krieger believes agentic AI is fundamentally redefining what it means to be a founder. You no longer need to write code or raise significant capital to start building. The bottlenecks, he says, have shifted to decision-making and operational friction—like managing merge queues. And the numbers support this momentum. In its first week of launch, Claude 4 reportedly tripled Anthropic's subscriber base and now accounts for more than 60% of the company's API traffic. Usage of Claude Code, its specialized AI coding agent, has spiked nearly 40%, drawing interest from both developers and nontechnical builders. Krieger shared that some users have even begun treating AI agents less like tools and more like capable creative collaborators. 'AI models can now function like an entry-level worker, and that is going to have a big impact on the workforce. We think we need to talk about this so we can prepare our economy and our society for this change, which is happening very fast,' he says. 'It's too late to stop the train—but we can steer it in the right direction.' A few weeks ago, Anthropic CEO Dario Amodei predicted that 'the first one-employee billion-dollar company' could emerge as soon as 2026, enabled by AI. He also suggested AI could eliminate half of all entry-level jobs within the next five years—a claim that drew immediate pushback from some in the tech industry. Among the skeptics was Google CEO Sundar Pichai, who cautioned against overestimating the reliability of AI systems like Gemini. 'Even the best models still make basic mistakes,' Pichai said during the recent Bloomberg Tech Summit in San Francisco. 'Are we currently on an absolute path to AGI? I don't think anyone can say for sure.' On the prospect of AI displacing the workforce in the near future, Pichai remained measured. 'We've made predictions like that for the last 20 years about technology and automation,' he said. 'And it hasn't quite played out that way.' Yet even amid skepticism, a quieter revolution is unfolding beneath the surface of agentic AI—one that's reshaping how work itself is defined in the era of intelligent software collaborators. MCP: The Infrastructure That Makes AI 'Work' The unsung hero behind Anthropic and Claude's leap in capability isn't just the model itself—it's the Model Context Protocol (MCP). While Claude 4 is praised for its intelligence and natural language fluency, MCP is the system-level breakthrough that enables it to move from passive assistant to active collaborator. This open standard allows Claude's AI agents to securely interface with tools like GitHub, Stripe, Webflow, Notion, and even custom internal systems. As a result, Claude isn't limited to answering prompts. It can pull real-time analytics, trigger actions, update databases, launch web assets, and manage entire project pipelines. Just as http enabled browsers to interact with websites, MCP is creating a universal interface layer for AI agents to operate across digital tools. 'Previously, AI agents were largely isolated—they could process information you gave them, but they couldn't directly interact with your actual tools and systems,' Krieger says. 'By solving the connection problem together, we're building infrastructure that will unlock entirely new possibilities for human-AI collaboration, making AI systems dramatically more useful and relevant in real-world contexts.' Major tech companies are already integrating MCP. Microsoft has built it into Windows 11, Azure, and GitHub, allowing AI agents to run workflows across OS and cloud infrastructure. Google has added it to Gemini SDKs to bridge model interactions with live apps. Companies like Novo Nordisk, GitLab, Lyft, and Intercom are also deploying Claude agents into live workflows. In this light, Amodei's 'one-person unicorn' prediction seems less like hype and more like a reflection of a deeper platform shift. 'As developers build new connections between knowledge bases, development environments, and AI assistants, we're seeing the early emergence of the more connected AI ecosystem we envisioned,' Krieger says. 'As AI assistants become more agentic, MCP will evolve to support increasingly sophisticated workflows. [MCP] might be the most important thing Anthropic has ever shipped.' Agentic AI Is Redefining the Modern Startup Tech Stack Krieger sees the combination of Claude 4 and MCP as a genuine platform shift—one where the AI acts like a partner rather than just a productivity tool. He describes Claude Opus 4 as Anthropic's most powerful agentic model yet and the world's best coding model. '[Opus 4] can work autonomously for nearly seven hours, which transforms how teams approach work. When I can prototype something in minutes, that fundamentally changes what's possible for a single person,' Krieger says. 'In my experience, it mirrors how people manage their work. That level of autonomous task execution just wasn't possible before.' With MCP in play, Claude becomes more than an assistant. It can push code, analyze logs, manage documentation, and send updates—without the constant context switching that slows teams down. In some cases, Krieger says, it simulates workflows that once required coordination across multiple departments. 'When you can iterate at speed, every manual process, every unnecessary meeting becomes this jarring interruption,' he noted. Still, not everyone is convinced that AI-powered unicorns are imminent. Analysts caution that while AI agents can automate many workflows, they can't yet match the experience seasoned professionals bring. 'The state of LLM-based AI agents is that you must give them simple decisions to make to reliable answers. We are not close to being able to throw a bunch of data at an AI agent and trust its decision,' Tom Coshow, a senior director analyst at Gartner, tells Fast Company. 'Is there an automatic VP of sales ready to go? Not even close.' Coshow emphasizes the need for realistic expectations. 'It's important to get real about what you can and can't build,' he says. 'No-code design is incredibly powerful, but it also creates this illusion that anything you type into the box will just magically work. It doesn't.' Building robust AI agents for real-world business use, he explains, is far from trivial, noting, 'Complex agents are hard to get right. LLMs are inherently probabilistic, and most business processes simply can't rely on that kind of unpredictability.' A Brave New Startup Era? Anthropic's core bet reflects its broader philosophy: We're moving toward a world where major chunks of work are automated. 'It's better to be aware of the risk and adjust to the change than to take the chance and be caught unprepared,' Krieger says. 'We're seeing this shift begin with tech companies, but it's going to move quickly into other knowledge-intensive industries.' So, is the one-person unicorn just hype—or a sign of things to come? It may still be too early to know. For experts like Coshow, the future lies not in abrupt disruption, but in careful evolution. 'The path forward is well-designed agentic workflows with a human in the loop,' he says. Whether or not a billion-dollar solo startup emerges by 2026, the tools to build one are already here. And that, as Krieger sees it, changes everything. 'It's going to be about finding people who can work at the intersection of customer problems and AI capabilities,' he says. 'The most valuable early hire might not be a traditional engineer—it could be someone who translates needs into iterative, AI-powered solutions. The one-person unicorn will be relentlessly curious, and fluent in working with intelligent collaborators.'

DOWNLOAD THE APP

Get Started Now: Download the App

Ready to dive into the world of global news and events? Download our app today from your preferred app store and start exploring.
app-storeplay-store