.png)
Artificial intelligence tools like ChatGPT are quickly becoming part of modern go-to-market workflows. Marketing teams use them to generate campaign ideas, sales teams summarize research, and leadership teams explore insights faster than ever before.
But one factor strongly influences the value you get from these tools: how you ask the question.
This is where prompt engineering comes in.
Prompt engineering simply means structuring your instructions to AI tools in a way that produces more useful, relevant, and accurate responses. For marketing leaders and GTM teams, learning a few simple prompting techniques can significantly improve productivity and decision-making.
In go-to-market teams, work moves quickly and priorities shift fast. Marketing and sales teams are often balancing multiple tasks at once, such as:
research target accounts
generate messaging ideas
prepare sales materials
summarize market insights
AI tools can support these tasks, but generic prompts often lead to generic results. When prompts are structured well, AI becomes far more effective at producing useful insights and structured outputs that marketing and sales teams can actually work with.
For leaders responsible for account-based marketing (ABM) and GTM execution, this means faster research, clearer messaging, and better internal alignment.
One simple but powerful technique is assigning the AI a specific role or perspective.
For example, instead of asking a general question, you might frame the prompt like this
You are a B2B marketing strategist helping a SaaS company refine its go-to-market messaging.
By defining a role, the AI adjusts its tone and focus. This often leads to more practical answers that align better with business needs.
For GTM teams, this approach can be useful when generating:
The responses tend to feel more relevant because the AI is guided toward a specific professional context.
Go-to-market activities often involve complex tasks. For example, preparing an ABM campaign might require research, messaging development, and content creation.
Instead of asking the AI to do everything at once, it often works better to split the process into steps.
A simple workflow might look like this:
.png?width=1500&height=400&name=March%204%20event%20blog%20Andris%20quote%20(2).png)
This step-by-step approach usually produces more structured and useful results than asking for a complete strategy in a single prompt.
AI performs better when it understands the context of the task.
For GTM teams, this might include details such as:
the industry you are targeting
the type of company (enterprise, mid-market, startup)
the role of the buyer (CMO, CTO, CFO)
the stage of the customer journey
For example, a prompt like this provides much clearer direction:
Explain the main challenges CMOs in enterprise SaaS companies face when scaling demand generation.
Adding this type of context helps the AI produce insights that are more aligned with real GTM scenarios.
Another helpful technique is simply telling the AI how you want the information presented.
For busy executives and marketing teams, structured outputs are far easier to review. Instead of long paragraphs, you might ask for:
a short summary
key insights
a comparison
messaging ideas
For example:
Summarize the key challenges for CFOs in manufacturing companies in three short points.
Small formatting instructions like this make AI outputs easier to integrate into presentations, strategy documents, or campaign planning.
Socratic prompting is a technique where you guide the AI through a series of questions rather than asking for an immediate conclusion.
This can be particularly useful for leadership teams exploring strategic questions.
For example, instead of asking:
What should our messaging be for financial services companies?
You might explore the topic step by step:
What are the main challenges in the financial services industry today?
How do these challenges affect marketing leaders?
Based on those insights, what messaging angles might resonate?
This approach often produces deeper insights because the AI works through the reasoning process.
Modern AI tools are increasingly multimodal, meaning they can process different types of input such as text, images, or screenshots.
For GTM teams, this opens up interesting possibilities. Teams can upload screenshots of competitor messaging, campaign visuals, or charts and ask the AI to analyze them.
This can help with tasks such as:
reviewing marketing materials
summarizing research documents
extracting insights from presentations
Multimodal capabilities make AI tools more flexible for everyday business workflows.
Finally, it is important to remember that prompting is rarely perfect on the first attempt.
Most users refine their prompts gradually. They start with a question, review the response, and then adjust the instructions to make the output more specific or structured.
Over time, teams develop a better understanding of how AI responds and how to guide it effectively.
Prompt engineering does not need to be complicated. In fact, a few simple practices can dramatically improve how AI tools support go-to-market teams.
Providing clear context, guiding the AI through structured steps, and defining the desired output format can turn generic responses into practical insights.
For leaders working in ABM, marketing strategy, and GTM operations, these small adjustments can help AI become a much more valuable assistant in everyday decision-making and planning.
Prompt engineering and AI tools can help teams move faster, but they are only one part of a successful go-to-market strategy.
If you are looking to strengthen your GTM approach, build more effective ABM programs, or explore how AI can support your marketing and sales teams, our team would be glad to help.
-1.png)
Anett Kóczán