Introduction

AI is transforming the way startups do business, but there are right and wrong ways to use these tools. 

Weak prompts result in generic responses that aren’t useful for your startup. But thorough, well-thought-out prompts will lead you to valuable responses that fit into your workflows.

Wait, what is a prompt, exactly? Prompts are the inputs you use to talk to AI. When you give AI instructions or ask a question, those are prompts. 

However, AI responses are engineered in specific ways, so strengthening your prompts is important if you want workable responses (or outputs) that pertain to your business, rather than generic fluff that won’t help your startup stand out from every other company using AI today.

This guide will walk you through how to communicate and collaborate with AI tools more effectively through improved prompting. From prompt best practices to how to address common mistakes and AI quirks, keep this guide handy as you open your go-to AI chatbot.

TL;DR: Key takeaways on AI prompting techniques for startups

• Provide detailed context and assign roles: Give the AI specific background information about your task, audience, and goals, then define the role the AI should play (e.g., "You are a SaaS sales expert writing for B2B founders").
• Specify exact format and output requirements: Instead of accepting generic bullet points, request specific formats, such as "3 tweet-length headlines without dashes" or "150-character meta description."
• Use your company's actual data: Connect AI to your CRM, upload reports, or reference specific company information to get customized outputs that match your brand and business needs.
• Iterate and refine responses: Don't accept the first AI output; use follow-up prompts to adjust tone, length, or focus until you get actionable results that fit your workflow.
• Avoid common mistakes: Don't use vague prompts, ask too much at once, or treat AI like a search engine; instead, approach it as a collaborator with specific business goals.
• Apply Chain of Thought prompting: For complex requests, break tasks into intermediate steps or ask AI to "think step by step" to get more thorough, reasoned responses.

Why does prompting matter for startups?

Prompt engineering matters to startups because it’s how you can glean actionable, relevant outputs from AI models.

Chatbots are designed to follow instructions. If your instructions are vague or aren’t tied to company-specific data, AI’s outputs will be just as vague.

Say you want to create a blog post as part of a marketing campaign, and you use AI to help. If you’re not specific, that blog post will sound just like every other blog post on the same topic, and it’ll get lost in search algorithms.

Crafting a blog that matches your startup’s tone and incorporates specific company data, such as product knowledge, will make it stand out.

The stronger your prompts, the better your outputs will be. That allows you to make decisions or take action faster, which is especially important for lean, time-crunched startup teams.

Just to reiterate how important strong prompting is, here are a handful of benefits you can get by improving your AI prompts:

• Improved accuracy and relevance
• Higher productivity
• Faster decision-making
• Improved customization and personalization
• Better consistency
• Boosted creativity and unique results
• Mitigated bias and increased fairness in outputs
• Better ROI

Popular AI chatbots to help your startup

Before diving into how to polish your prompts, let’s take a look at some of the most popular AI chatbots for startups.

• ChatGPT: One of the most widely used chatbots, meaning it pulls from a huge database. Prompting on ChatGPT can be helpful for market and competitor research, but less useful for business-specific outputs.
• Claude: Claude’s strength lies in content creation, especially as startups can build out project knowledge to better guide prompts across campaigns.
• Gemini: This AI chatbot, created by Google, integrates with Google apps like Mail and Workspace for seamless workflows.
• Copilot: Like Gemini, Microsoft’s Copilot integrates with Microsoft, offering better consistency for startups utilizing Microsoft tools in their workflows.
• Breeze: HubSpot’s Breeze chatbot operates directly within the HubSpot app, utilizing your startup’s specific data to provide highly tailored responses, analytics, and recommendations that enhance content, engage customers, and generate leads. The HubSpot Community even offers Breeze prompts to help you get started.
• Perplexity: Perplexity operates similarly to a search engine but with a powerful boost from AI. This tool may be helpful in the research stage for product development or target audience research.

Startups may consider using standalone and/or business tool chatbots, depending on use cases. For instance, using a standalone option like ChatGPT can draw from a vast data pool but may offer less tailored responses, whereas business tools like Breeze enable you to tie the chatbot directly to your company's data.

Ultimately, many startups can use multiple different AI chatbots for more comprehensive results. You can start your research on Perplexity, develop a more general output with ChatGPT, and then tailor the outputs using your specific data with Claude or Breeze.

No matter what AI chatbots you end up incorporating into your tech stack, you’ll need to understand how to fine-tune your prompts to get the most out of your AI helpers.

Prompting techniques

It’s time to start inputting prompts and getting AI to work smarter for your business. We’re going to dive into five steps for improving prompts, with plenty of detailed explanations and examples to guide you.

1. Clearly provide context

The more context you can provide, such as about the task, the audience, the tone, and the goal of the response, the better the AI can tailor its response based on those directions.

Be clear and direct, using action verbs to request specific results from the prompt. This method is useful for any type of prompting, but it’s especially important when using zero-shot prompting, where you’re simply giving direct instructions without examples.

Include the target audience and provide relevant facts and details surrounding the situation. Pretend the AI is a new team member, and you’re providing as much information as possible to help that team member complete a task to the best of their ability.

Example prompt:

“Draft a follow-up email for a lead who opened our pricing page but didn’t book a meeting.”

2. Assign a role to the AI

Now, let’s build upon the context we’re providing AI.

AI offers a blank slate, and each time you give it a prompt, you may be using it in a different way. Sometimes, you may consult AI as a market researcher, but other times, it may be a marketing pro.

Always define AI’s role within the prompt to help guide the tone of its response and signal it to respond to the target audience appropriately. 

Example prompt:

“You are a SaaS sales expert writing for early-stage B2B founders.” After this, you’d follow with a clear, actionable statement, such as one to write an email or a blog post.

3. Specify format and output

Don’t hit enter just yet; AI also requires additional information in order to provide optimized outputs. Many AI models tend to submit outputs in easy-to-digest bullet points, but that format doesn’t work for every situation. 

Other critiques of AI responses include reliance on elements such as em dashes and emojis. If that style of writing doesn’t work for your business, make sure to specify in the prompt to avoid these tactics.

You may consider editing the bullet points and emojis out later, but you’ll be surprised by how long it takes to turn several bullet points into cohesive paragraphs that are not robotic in tone. 

Instead, get content that is more usable from the start by improving your prompt instructions. Writing a better prompt takes less time than editing and rewriting AI outputs (trust us!).

Example prompts:

• “List three email subject lines in bullet format.” 
• “Give me three tweet-length headlines for a blog post without any dashes.”

4. Refine and iterate

While writing a stronger prompt will minimize time spent editing, it won’t completely eradicate the need to review and update AI outputs.

Even with direct, specific inputs, AI may spit out results that don’t quite match the directions you gave. You could specify a 150-character meta description or a 200-word LinkedIn post, only to have AI provide a 300-character meta description or a 500-word post for social media.

That’s when it’s time to respond with follow-up prompts. You and the AI tool can write back and forth, iterating until the response is refined and more usable.

For complex requests, you may consider one-, few-, or multi-shot prompting, where you provide one or more examples to better guide the AI to achieve what you’re expecting.

Another point to consider: at this stage, you may want to request AI to explain its outputs. Explanations will help you better understand why AI is giving you certain responses, allowing you to improve your follow-up prompts. Requesting reasoning from AI outputs is known as chain-of-thought prompting. Similarly, you can request AI to “think step by step” after a prompt, which is called zero-shot chain-of-thought prompting.

Example prompts:

• “Make the tone more casual without inserting emojis.”
• “Shorten the length to five sentences or less.”
• “Shorten the title to 60 characters.”

5. Use your existing data

Connecting to company data will offer the most customized results, and this is where business AI chatbots really shine. 

For instance, integrating CRM data with Breeze enables you to research and analyze data, create marketing content based on customer demographics and sales data, personalize sales outreach to increase lead conversion, and more.

Some tools will connect directly to your data, but others will require you to upload it manually. For example, with Claude (Pro or Max accounts), you can set up projects within your organization’s workspace. Then, the AI can directly pull from project knowledge or prompt attachments, such as reports or other documents, to provide company-specific outputs.

Incorporate your startup’s data as much as possible for the best prompts and outputs. Whether you’re tailoring marketing materials or following up with a new lead, adding existing startup data to AI training or individual prompts means responses will be the most relevant to your brand and needs.

Example prompts:

• “Use our homepage messaging to write this email.”
• “Summarize our last 30 days of CRM activity by stage, including counts and conversion rates for each stage. Format it as a bullet list.” (Then, you can add a follow-up of “Now turn that into a short update I can paste into our weekly investor email.”)

Applying AI prompting techniques across startup functions

So, what does AI prompting look like in the real world? From sales and marketing to product development, consider these ways to prompt AI to help complete a wide range of tasks for your startup.

Marketing

According to HubSpot, 98% of marketing organizations are investing in AI, and marketers are utilizing this technology to increase productivity, enhance personalization, gain deeper insights, and save time across various marketing functions.

Based on the 2025 State of Marketing Report, here’s how marketers are using AI:

• Brainstorm content ideas
• Write campaign copy
• Develop SEO briefs
• Craft social media posts
• Write emails
• Conduct research

Example prompts:

• “Draft a LinkedIn post announcing our Series A funding, in a confident but humble tone.”
• “Attached is a transcript from our recent [EVENT NAME]. Write a blog post summarizing the event and major takeaways.”

Sales

From sending a cold email to following up on a warm lead with personalized messaging, AI can be another valuable collaborator on your sales team. As of 2024, AI use in sales has increased from 24% in 2023 to 43%, based on HubSpot’s AI Sales Trends Report. 

Sales teams are utilizing AI for lead qualification, prospecting, research, training, and other purposes. Here are some specific use cases for AI in sales:

• Writing cold emails
• Crafting personalized follow-up emails
• Developing product demo invites
• Qualifying leads
• Automatically updating CRM information from customer conversations
• Generating reports and insights
• Predictive forecasting

Example prompts:

• “Write a cold email for our B2B automation tool targeting HR managers at tech companies.”
• “Write a personalized follow-up email to send to high-quality leads that inquired about [PRODUCT NAME].”

Customer support and ops

AI is particularly useful for making operations more efficient and improving customer support, especially for startups with a lean budget and a small number of employees.

Startups can use AI for customer support and ops functions, such as to:

• Create help articles
• Write user guides
• Automate ticket replies
• Establish 24/7 support, even for a small team
• Improve workflows

Example prompts:

• “Summarize the top 10 most common support tickets from the past month. Group them by theme and suggest possible fixes or automations.”
• “Help me write a professional, empathetic support response to a frustrated customer with a problem [EXPLAIN ISSUE]. Include three troubleshooting actions they can try.”

Product and strategy

No, AI can’t dream up the next market-disrupting product for you, but it can serve as a collaborator to help you expand your own innovative ideas. 

Whether you’re developing a new product or preparing a go-to-market strategy, here are ways to use AI for product and strategy functions as a startup:

• Produce market research summaries
• Enhance roadmap planning
• Analyze trends among existing customers and product sales
• Automate product descriptions
• Detect bugs and other product risks
• Increase product testing efficiency
• Automate and analyze customer feedback

Example prompts:

• “Summarize the top three customer pain points mentioned in our last 20 NPS survey comments.”
• "I have these five feature requests from users: [list features]. Rank them by potential impact on user retention for a B2B SaaS with 500 SMB customers. Give a one-sentence rationale for each ranking."

What are some common AI prompting mistakes?

There are numerous best practices for prompting AI to achieve better outputs, but startups also frequently make mistakes when incorporating AI into their businesses.

From over-relying on AI in place of human knowledge to failing to tie prompts back to business goals, here are some common mistakes startups make when using AI (and some suggestions for how to correct these issues).

Vague prompts

Vague inputs equal vague outputs. Without being specific in your requests, AI will have to generate responses based on its general training, resulting in generic outputs that may be less relevant to your startup.

Instead, be specific. Include specifics about your business and clearly define the goal and format requests for the output.

Example of a vague prompt:

"Help me with my business strategy. I need to determine the next steps for my company and how to grow it effectively. What should I focus on?"

Correction: 

"I'm the founder of a 6-month-old B2B SaaS startup with 200 beta users and $15K MRR. Our HR analytics tool helps mid-size companies (100-500 employees) track employee engagement. We have 3 months of runway left and need to decide between: (1) focusing on product improvements to increase user retention from 60% to 80%, or (2) hiring a salesperson to accelerate customer acquisition. Please analyze both options considering our current metrics and recommend which path to prioritize. Include three specific actions for whichever option you recommend and explain the expected timeline to see results."

Asking too much in one prompt

While you don’t want to be overly vague, it’s also important not to weigh down your input with too many tasks at once. If the prompt is filled with unnecessary or excessive information, it will be harder for the AI to handle all requests within the prompt adequately.

More complex prompts are better suited for Chain of Thought prompts, where you can include your requests as a list of intermediate steps to better guide the AI through every task and all expectations.

Example of a lengthy prompt: 

"Help me create a complete business plan for my fintech startup, including market analysis, competitive landscape, financial projections for 5 years, marketing strategy, product roadmap, hiring plan, fundraising strategy, legal structure recommendations, partnership opportunities, risk assessment, exit strategy, brand guidelines, website copy, investor pitch deck, and operational procedures. Also, help me name the company and design a logo concept. I need this to be comprehensive and professional since I'm presenting to investors next week."

Correction: 

"I'm raising a seed round for my B2B fintech startup that helps small businesses manage cash flow. I have product-market fit with 50 paying customers and $25K MRR. Please help me create a compelling two-slide financial projection section for my investor pitch deck. Include: (1) a 3-year revenue forecast based on our current $500 ARPU and planned customer acquisition, and (2) key assumptions about growth rate, churn, and unit economics that investors should understand. Base projections on these metrics: 15% monthly churn, $2K CAC, 18-month payback period."

Skip iteration

Even if you’ve created a detailed prompt and trained the AI on your business data, you don’t have to (and shouldn’t) just rely on the first response it provides. Submit additional prompts to help AI refine its initial output.

Correction: 

It’s easy to correct this mistake: add more follow-up prompts. Ask the AI chatbot to provide you with more examples, more details, more clarification, or alternatives to the original output. You can request AI to rewrite specific sections or adjust the tone of the entire output.

When iterating, you may need to provide additional information and data for the AI to work with. By iterating, you and the AI collaborate to fine-tune the response into its best version.

Using AI as a search engine, rather than a collaborator

AI may seem like another search engine, but it’s a common mistake to treat it like one. Instead, founders should approach AI like an advisor or collaborator. 

AI is both a tool and a member of the team, and founders who improve their prompts will get far more useful results than just general responses to basic search questions.

Example of using AI as a search engine: 

“What marketing metrics are most important for B2B startups?"

Correction: 

"My B2B project management SaaS currently has a $1,200 CAC through paid ads, 8% monthly churn, and $180 ARPU. Industry benchmarks suggest my CAC should be under $800 and churn under 5% for healthy unit economics.

Help me identify which metric to target first to enhance our path to profitability. Should I focus on reducing CAC by improving our conversion funnel, or reducing churn by enhancing onboarding? Analyze the financial impact of improving each by 25% and recommend a specific 90-day action plan for whichever you think will have the greater impact."

Not using AI to clarify ideas

It’s not all data analysis and market research when it comes to AI. You can use this tool to refine or validate your creative business ideas, ultimately strengthening them and preparing them for launch.

Example of not using AI for idea clarification:

"Write a marketing campaign for our productivity app targeting remote workers. Make it compelling and professional."

Correction: 

"I have a rough idea for marketing our productivity app to remote workers, but I'm not sure if I'm thinking about it right. My initial thought is that remote workers struggle with staying focused at home, but I'm wondering if the real problem is more about collaboration or maybe work-life balance?

Help me think through what the core pain point should be for our messaging. What questions should I be asking to better understand what remote workers actually need from a productivity app?"

Not applying AI outputs to real-life workflows

Perhaps not every output from AI ultimately works for your business; however, the purpose of this tool is to refine the outputs with human logic and creativity, then implement them in a way that suits your startup.

Don’t leave AI’s best work confined to the chat box. Write prompts that promote actionable outputs.

Example of not applying AI outputs:

"Help me create a customer onboarding checklist for our SaaS product." (However, afterward, the founder doesn’t implement the checklist into the business.)

Correction: 

"Help me create a customer onboarding checklist that I can implement in our current setup. We use [TOOL NAME] for messaging, [TOOL NAME] for documentation, and email sequences through [TOOL NAME]. Format the checklist as specific action items that map to these tools, and include who on my three-person team should own each step. I want to launch this next week."

Prompting without a clear business goal

Don’t use AI just for the sake of using AI. Like with any tool you fold into your business operations, it should serve a purpose. That means when you’re prompting it, tie your requests to actual goals or milestones you want the business to achieve. This method allows the AI to produce goal-driven outputs that will help your startup move forward.

Example: 

"Can you help me think about my e-commerce startup? We sell sustainable clothing online and have been running for 8 months. We have some customers and decent sales. I'm wondering about social media and whether we should expand our product line. Also thinking about our website design and customer service. What do you think we should do?"

Correction: 

"My sustainable clothing e-commerce startup needs to increase monthly revenue from $25K to $50K within 6 months to hit our Series A fundraising target. We currently have 2,000 customers with a 25% repeat purchase rate and $85 average order value.

I'm considering three growth strategies: (1) expanding our Instagram marketing to reach younger demographics, (2) launching a men's clothing line, or (3) implementing a customer referral program.

Analyze which strategy is most likely to help us double our revenue within this timeframe, considering our current customer base and the $15K monthly marketing budget available. Provide specific reasoning for your recommendation."

Listing tasks

Unfortunately, AI doesn’t respond helpfully when you just give it a laundry list of chores. Descriptive, informative prompts that are goal-focused better steer AI to deliver relevant, usable outputs.

Example of listing tasks:

"I need to do the following for my healthtech startup:

• Write a job description for a software engineer
• Create social media posts for this week
• Draft an email to potential investors
• Update our privacy policy
• Plan our product roadmap
• Research competitors
• Design a new logo
• Write blog content about health trends
• Set up Google Analytics
• Create a customer survey

Can you help me with all of these?"

Correction: 

"My healthtech startup is preparing for a Series A raise in 3 months. Our main challenge is that investors keep asking about our competitive differentiation and user growth strategy.

I need to create compelling materials that address these concerns. Help me prioritize and tackle the most impactful task first: should I focus on (1) creating a competitive analysis document that clearly shows our advantages, or (2) developing a user acquisition strategy with projected growth metrics?

Consider that we currently have 5,000 users and $50K MRR, but limited data on how we compare to competitors. Whichever you recommend, please provide a specific outline for how to approach that single task effectively."

Expecting AI to replace team thinking

We’ve said before that incorporating AI into your workflows is similar to working with a team member. Yes, AI is a collaborator, but it won’t replace human brainpower.

Correction:

AI usage can be a starting point, but always pair AI with human supervision. Always review AI responses, and leave final decisions to the humans in charge. Also, listen to employee feedback to inform how your organization uses AI.

Lack of team training on AI

According to a report from Business.com, only 52% of small- and medium-sized businesses are training employees on AI, yet over half of employees at SMBs feel that they need more training.

But AI training helps make the most of this tool, with 90% of employees trained on AI reporting improved performance.

Correction:

Your startup needs an AI usage policy, and you should establish training materials for employees who will work with AI. 

Not only will a usage policy and training help employees work more efficiently with AI, but it also limits mistakes and ensures your company’s AI usage follows laws and ethical guidelines. Without an AI usage policy, your startup is at risk of costly and potentially business-ruining mistakes.

Watch for these AI quirks

AI isn’t perfect, and just like humans, it can make mistakes. However, by sharpening your prompting skills, you can reduce errors or quirks and strengthen AI responses.

Here are some examples to consider.

Final thoughts: making AI prompting a core startup skill

Prompting isn’t just some tech trend. Founders today need to treat AI prompting like what it really is: an essential communication skill for modern businesses.

Whether you’re using AI for research or incorporating it into your business operations, it’s important to understand how to write strong prompts to get the best results from this technology.

While this guide offers several examples and tips for writing AI prompts, the real learning comes from experimenting, collecting feedback from your employees and customers who interact with your AI, and iterating until you achieve the desired results.

Ready to make AI work for your scaling startup? Explore AI Decoded, a free pocket guide to help you find the right AI tools for your business and improve your prompts to get 55% better results from AI.