How to ask AI the right questions

Knowing how to ask AI the right questions is essential if you want to gain insights from your prompting. The quality of your queries can be the difference between responses that state the obvious and recommendations that drive action.

For instance, “How can I improve my sales?” will get you generic tips, but asking “Which part of my sales cycle loses the most leads, and how can I fix it?” helps you strategise. A small shift in the way you query turns surface-level information into insights you can act on.

And making that shift is more important than ever. As per McKinsey’s latest survey , 78% say their organisations use AI in at least one business function, up from 72% in 2024 and 55% a year earlier. When almost every business is experimenting with AI, simply using it is no longer a competitive advantage. The businesses that get the edge will be the ones that use it right.

This guide will explain how to craft effective AI questions. We’ll explore how a shift in the way you query AI can be the difference between helpful and unhelpful responses, and provide practical examples to show you how to transform generic answers into actionable intelligence.

• What different types of AI can (and can’t) do
• The way you ask questions matters
• Transforming simple questions into strategic insights
• Types of questions you can ask AI (and how to elevate each)
• Crafting effective AI questions – 5 advanced tips
• Common mistakes when asking AI questions
• The concern with customer-facing AI (and how AI agents and data address the problem)
• Summing up

First, you need to understand the kind of AI you’re working with. Not all tools think or work the same way, and their capabilities determine what you can ask and how helpful the responses will be.

Broadly speaking, there are three types of AI models:

• Rule-based AI models use ‘if-this-then-that’ logic, following predefined rules to deliver predictable outputs. They’re great for automating repetitive tasks like approval systems or customer service FAQs, but fall short for tasks that require nuanced judgment.
• Machine learning models learn from historical data to make predictions. Unlike simpler models, they aren’t tied to set rules, meaning they can answer complex problems and learn as they’re exposed to more data.
• Neural networks take things a step further by mimicking the way humans think. These systems can ‘step outside’ of their training data to make nuanced assumptions based on context rather than facts. This is the tech that powers your favourite generative AI tools.

A rule-based model can give you predefined AI answers, but it can’t provide actionable strategies tailored to your business. Machine learning models are a step up. However, they’re limited by the data they’re trained on, so their predictions can sometimes miss emerging trends.

For deep insights, it’s a good idea to invest in a neural-network-based system. Solutions like Agentforce combine predictive and generative AI to understand your business data, interpret natural language prompts, and provide insights tailored to your goals and customer context.

Equipping yourself with the right tools lays the foundation for better AI outcomes. That said, even the most powerful neural networks are only as good as your AI prompts. To gather insights that support decision-making, you need to understand how to ask questions that get results.

We’ve broken down AI questioning into three levels – simple, contextual, and strategic.

Simple questions are fine for quick lookups, but they won’t help you make decisions or analyse complex problems. Contextual queries are a level up. They help AI connect your prompts to data points and business goals.

However, the true value begins when you can ask AI to recommend the best actions and next steps. Strategic questions push AI beyond a basic answer generator to produce responses that inform decisions. This is the secret to gathering insights that drive growth.

So, the skill isn’t knowing what kind of things to ask AI. It’s about knowing how to elevate any question from a simple query to a strategic prompt that makes AI do the hard work for you.

Take a simple prompt like:

“Why are my customers unhappy?”

This question might net you a broad summary of reasons customers aren’t satisfied, like poor communication or high prices, but it’s too vague to guide meaningful action. To improve this prompt, we need to make it contextual by providing more information. Here’s an improved example:

“What key drivers are lowering CSAT scores among repeat customers in Australia this quarter?”

This adds a who (repeat customers), a what (CSAT scores), a where (Australia), and a when (this quarter), helping the model deliver a clearer picture of what’s happening in the context of your business.

After that, all that’s left to do is make this strategic by adding intent and scope:

“Why are these metrics impacting CSAT among repeat customers in Australia, and what steps can my marketing team take to improve this over the coming quarter?”

Now, AI understands why you’re asking your question. This helps it recommend next steps tailored to your unique problem. With a small change, you’ve turned generic information into an actionable plan your teams can apply to their workflows.

This mindset shift from simple queries to decision-focused prompts is what separates the businesses that use AI from those that use it well.

Again, having a tool that can handle this kind of prompt engineering is key. Solutions like Agentforce can interpret natural-language queries and (in tandem with the full suite of Salesforce AI tools) produce targeted insights that are valuable, accurate, and grounded in the context of your business.

Now that we’ve discussed how you can improve your AI questioning, let’s take a closer look at the types of prompts to which you can apply this school of thought.

Together, these four question types create a framework for understanding data, exploring patterns, making strategic decisions, and turning those decisions into actionable tactics. Let’s explore each of these question types one by one.

Factual questions are the most straightforward type of AI query. They’re designed to retrieve information like definitions, metrics, or explanations. Think of them as the foundation layer when you want to understand what something is before you ask why or how.

While factual answers aren’t used to drive decisions directly, there are still ways to make this type of query more strategic. Adding context and intent can guide the AI model to provide more accurate, relevant responses.

• Simple - “What is our customer churn rate?”
• Contextual - “What was our customer churn rate last quarter in the UK market?”
• Strategic - “How does our UK customer churn rate from last quarter compare to industry benchmarks, and what factors are contributing to the difference?”

The more specific you can be, the more accurately AI can ground its responses in your business context. This gives you a stronger foundation for analytical and operational follow-up questions.

Analytical questions help you make sense of your business data. Rather than just retrieving numbers or facts, these queries prompt AI to interpret patterns and uncover trends. You can think of them as the bridge between what happened and why it happened.

Simple analytical questions can be useful, but their value increases when you can add additional context and tie them to your business goals. Here’s an example:

• Simple - “What trends are emerging in our sales data?”
• Contextual - “What trends are emerging in our AUS sales data by product category compared to Q4 last year?”
• Strategic - “What factors drove those AUS sales trends last quarter, and how can we apply those insights to forecast performance in other regions?”

The more details you include, the easier it is for the AI model to analyse in the context of your organisation. This bridges the gap between raw data and insights, supporting more action-oriented operational questions.

Whereas factual and analytical questions help you understand what’s happening and why, operational questions tell you what to do next.

These prompts aim to get AI to support decision-making. By adding context and intent here, you can guide the model to translate insights into actionable recommendations that show teams where to focus their efforts.

• Simple - “How do we improve our website conversion rate?”
• Contextual - “How can we improve our website conversion rate for mobile users from paid search in Australia this quarter?”
• Strategic - “Based on user behaviour and past campaign performance, what changes should our marketing and web teams prioritise to increase mobile conversion rates from paid search in Australia this quarter?”

The key is to specify enough context so that AI can ground recommendations in your business’s needs. Asking the model to prioritise next steps and assign them to relevant teams also provokes AI to build out an actionable plan rather than just a broad list of tips.

Creative prompts help to generate content, materials, and ideas, such as marketing campaign concepts, workflow optimisations, and customer engagement techniques. In essence, they translate broader operational strategy into concrete tactics.

For instance, if you discovered through your operational questioning that mobile conversions from paid search are underperforming, a creative question could generate specific ideas for landing page tweaks or targeted campaign messaging to improve that metric.

Again, the more specific you can be, the better. Here are some examples ranging from poor to excellent:

• Simple - “Give me ideas for a marketing campaign.”
• Contextual - “Give me ideas for a marketing campaign to promote our sustainable product line to Gen Z customers in the US.”
• Strategic - “Based on past engagement data, competitor activity, and performance goals for Q4, build a marketing campaign to drive conversions among US Gen Z customers for our sustainable product line, and lay out a roadmap to bring these ideas to life.”

By using factual, analytical, operational, and creative questions in tandem (and elevating each prompt to be more strategic), you create a framework to uncover and analyse data, make key decisions, build out strategies, and turn them into tactics you can act on.

Once you’ve mastered the basics of effective prompting, the next step is to learn how to refine your approach. In this section, we’ll lay out five advanced tips to ensure your AI interactions produce consistently valuable results.

1. Build on previous prompts
2. Refine your structure with few-shot prompting
3. Try assigning a role to AI
4. Ask AI to think out loud
5. Experiment and iterate

Let’s explore each of these tips in more detail.

AI is rarely at its best on the first attempt. The key is to know how to build on prompts to move from factual and analytical to operational and creative questions, providing the right context along the way. This is the art of conversational prompting.

For example, here are four prompts that, when inputted in a sequence, can help to build AI’s understanding and turn data into insightful outputs.

1. Factual - “What were our top-performing product categories last quarter?”
2. Analytical - “What factors contributed to the performance of these product categories?”
3. Operational - “What steps can our sales and marketing teams take next quarter to improve underperforming categories?”
4. Creative - “Generate campaign ideas to boost engagement for these underperforming categories based on the data above.”

If you began with the creative prompt, AI would lack the context to ground its ideas in your data and priorities. Following a sequence means each question builds on the result of the last, producing a more accurate, helpful output.

Few-shot prompting involves giving AI examples it can use to model its response. Think of it as an additional layer of context that tells AI how you want it to display results.

This technique doesn’t replace strategic questions, but rather adds onto them to shape the tone and structure of the AI’s output. To show how this works, let’s take one of our prompts from earlier and expand on it using few-shot techniques. The extra content is marked in bold.

This additional context works well for all types of queries. For instance, you might ask AI to write in bullet points for factual questions, follow a proven framework for analytical questions, or specify tone of voice to keep creative prompts on brand.

Are you having difficulty getting AI to think in the context of your business? Try assigning a persona or professional role to shape its language, tone, and strategy.

This is known as “role-based prompting”. For instance, you might say:

• “Act as a customer service representative and write an email to one of our customers apologising for a delayed delivery.”
• “As a data analyst, evaluate why our customer satisfaction dropped last quarter, referencing patterns in support ticket resolution times.”
• “You’re a marketing director. Based on this quarter’s engagement data, what campaigns would you prioritise next to drive conversions?”

This encourages AI to respond in a way that matches the thought processes and tone of a professional in the specified role. You could also flip this the other way to ask AI to explain problems from the perspective of your ideal customer.

Chain-of-thought prompting encourages AI to think out loud and walk through the reasoning behind its answer. This makes its logic clearer and more transparent.

Here are some example prompts:

• “Decide if our marketing campaign was successful by analysing budget, target audience engagement, and revenue increase. Walk through your analysis step by step.”
• “Propose three subject line ideas for an upcoming email campaign targeting lapsed customers. Explain the rationale for each and how it aligns with engagement trends.”

Asking AI to explain its reasoning helps you understand why certain actions are recommended. This is particularly powerful for complex or open-ended problems, as it makes it easier to see whether the suggested steps actually align with your business goals and priorities.

AI prompting is an iterative process. If a response feels “off”, tweak the question by rephrasing, adding additional context, or breaking the query into smaller steps.

Iterating your prompts over time will let you test how different phrases influence output. It’ll also give you more time to grow familiar with the AI model you’re using and understand how it interprets nuance and intent. This will be key to consistently high-quality responses over time.

To round off this part of the guide, let’s take a look at some common errors people make when asking AI questions, plus tips to avoid them.

You and your teams know that asking AI the right questions leads to better outcomes. But what happens when AI is engaging directly with your customers?

This is when things get tricky. Unlike internal teams that can learn how to master effective prompts, customers often input vague or incomplete questions to AI chatbots. A broad query like “My order is late” often causes the model to repeat itself in an attempt to gather information. This leads to delays, inconsistencies, and a frustrated customer demanding to speak to a rep.

To make customer-facing AI effective, customers must be able to ask questions in their own words and get accurate answers without understanding how to prompt an AI system to get the best results. The AI model also needs to be capable of “filling in the gaps” when customers input incomplete queries. So, what’s the solution?

AI agents go beyond static, rule-based chatbots by combining natural language understanding with access to business data. This means they can interpret customer queries when they’re vague, infer intent with limited context, and proactively retrieve any customer information needed to provide accurate responses in real time.

Salesforce’s Help Page is a great example of this concept in action. Previously, our Einstein Bots solution would handle a predefined set of customer queries. This worked well for simple problems, but if the customer had a more complex query, the bot couldn’t help. This left users waiting for a rep to manually solve their ticket.

To fix this issue, we replaced our traditional solution with Agentforce to handle all of our customer queries. This achieved three things:

• Customers can now talk to Agentforce in natural language
• Agentforce can access and pull data from customer records
• With that data, Agentforce can add context to user prompts autonomously

This means users no longer need to be AI experts or construct the perfect prompt to get the response they’re looking for. Agentforce bridges the gap by grounding itself in customer records, allowing it to fill in context automatically, even when a customer provides a vague input.

This means a vague query like “My order is late” becomes “My order #12345 from September 10th is delayed. What is the current status and estimated delivery date?”. Agentforce fills the context gaps automatically, transforming a potential delay into an easy-to-solve support task.

And it works. Agentforce achieves twice the performance of our older AI assistants and resolves 80% of enquiries without the need for a human rep.

So, while asking clearer questions is always going to be the best option, there are ways to tackle the problem if consistently clear queries aren’t achievable. If you can combine prompt training for teams with agentic AI that’s grounded in data and can fill in gaps in nuanced conversation, you’ve covered all your bases.

As we’ve seen, the questions you ask AI determine the quality of the insights you get in return. Knowing how to elevate prompts from simple to contextual and strategic will help you turn generic outputs into actionable intelligence that drives business-wide growth.

Asking the right AI questions starts with an AI system that can understand requests in the context of your business. Agentforce (along with the full suite of Salesforce AI tools) can help you build effective prompts, flesh out strategies, make smarter decisions and deliver personalised customer experiences at scale. Watch the demo today to get started.

Once you’re set up with the right advanced AI model, all that’s left to do is ask questions that put AI to work for your business. We’ve discussed an actionable framework to do so in this guide, but if you’d like to keep advancing your knowledge, visit Trailhead for a range of free courses designed to help you master AI prompting and apply it to real business scenarios.

Get started today with our free Prompts and Prompt Builder course to practice crafting effective prompts and producing insights for your business.