Unlock the potential of AI in competitive intelligence by mastering prompt engineering techniques that drive smarter, faster insights.

The Crucial Role of Prompt Engineering in CI

One of the standout themes from our Behind the Curtain webinar (find the recording here!) was just how critical prompt engineering is when working with AI, especially for competitive intelligence. As our Chief Product Officer Jon White shared, the best AI outputs come from giving the model clear roles, focused objectives, and high-context inputs. The takeaway? Better prompts = smarter insights.

But what does that actually mean in practice? In this post, we’ll explore what prompt engineering looks like in a CI context, along with real examples and tactics you can use to get better, faster insights from the AI tools already in your workflow.

Why Clear Roles and Focused Objectives Matter

AI tools are only as good as the instructions you give them. That’s true for every function, but in CI, where you're working with nuanced insight, mixed data sources, and variable audiences (sales, execs, product), clarity and context are everything.

A vague prompt like “summarize this article” might return fluff. But ask the AI to “summarize this article with a focus on go-to-market changes that may affect our positioning with mid-market buyers,” and you’re on the path to something useful. Prompt engineering is the skill of being specific. And for CI pros, it’s quickly becoming just as important as knowing how to build a battlecard or run a win/loss interview.

High-Context Inputs: The Key to Quality AI Outputs

Providing high-context inputs means giving the AI as much relevant information as possible to guide its response. This includes specifying the role the AI should assume, the objective it should achieve, and the context in which it should operate.

For example, instead of asking, “What are the main points of this report?” you might say, “You are a competitive intelligence analyst for a tech company. Summarize the main points of this report with a focus on emerging market trends and potential competitive threats.” This approach frames the task through the lens of your actual use case, improving tone and relevance.

Role Prompting: Enhancing Relevance and Tone

Role prompting helps the AI understand who it’s speaking as and what perspective it should take. This technique is particularly useful for CI tasks where the audience and context significantly impact the type of insights needed.

Instead of: “Summarize the insights from these reviews.”

Try: “You are a competitive intelligence and enablement professional responsible for creating content to help your sales team win more competitive deals. You work for Crayon. Your goal is to summarize these customer reviews to highlight areas where our competitor’s onboarding process causes frustration.”

This framing helps the AI generate outputs that are tailored to your specific needs and use case, enhancing both relevance and tone.

Chain-of-Thought Prompting: Improving Clarity and Completeness

Chain-of-thought prompting involves breaking down your prompt into ordered steps, allowing the AI to reason in a structured manner. This technique improves clarity and completeness, ensuring that the AI covers all necessary points in its response.

Example: “Start by identifying the top 3 themes in these insights. Then, write a short paragraph about each theme using examples. Finally, suggest a one-line takeaway for sales enablement.”

By guiding the AI through a series of steps, you help it produce more organized and comprehensive outputs, which are especially valuable in CI where detailed analysis is crucial.

Few-Shot Prompting: Setting Standards with Examples

Few-shot prompting involves showing the AI what “good” looks like by providing examples of the desired output. This helps the AI mirror the tone, format, and expectations, reducing the need for rework.

Example: “Here’s a sample win/loss summary from last month. Use the same structure and tone to create one for the insights below.”

Providing examples helps the AI understand the standards it needs to meet, leading to more consistent and high-quality outputs.

Contextual Prompting: Guiding AI with Specific Details

Contextual prompting involves adding company-specific details to guide the AI’s relevance. Generic prompts often result in generic outputs, but providing real context can dramatically improve the quality of insights.

Example: “Use these product launch notes and objection-handling scripts as context. Then summarize how Competitor A’s latest messaging might create confusion with our positioning.”

By incorporating specific details relevant to your company and its competitive landscape, you can generate more actionable and tailored insights.

Leveraging Tools like Sparks for Effective Prompt Engineering in CI

If you're using platforms like ChatGPT, Claude, or Gemini, these techniques can be applied directly, especially for exploratory work or ad hoc requests. But if you're working in a CI platform like Crayon, tools like Sparks bring these prompt engineering concepts into your daily competitive workflows, eliminating the need for copy/paste or new tabs.

Pre-Built Prompt Templates for Common CI Tasks

Sparks includes ready-to-use templates that apply proven prompting structures to specific CI use cases. These templates are built with role prompting, chain-of-thought, and context-aware inputs already baked in, ensuring best practices by default. Examples include:

• Summarizing product reviews by competitor
• Surfacing new messaging changes from webpages
• Generating a monthly competitive update email
• Drafting positioning summaries for new market entrants

Custom Prompts When You Need Flexibility

For CI pros who want more control, Sparks allows you to write your own prompts from scratch and apply them across any set of insights. Whether you want to generate a summary in your CEO’s tone or compare two competitors' positioning strategies side by side, Sparks provides the flexibility to tailor prompts to your specific needs.

AI Toolkit to Support Prompt Exploration

Sparks also includes a prompt “toolkit” with example phrasing, starter prompts, and guidance for different use cases. This toolkit helps bridge the gap between inspiration and execution, making it easier to test new prompt structures or refine Spark outputs.

Why It Matters

CI pros are being asked to deliver insights faster than ever and do it in a way that’s clear, strategic, and audience-ready. Prompt engineering is how you get there, and Sparks gives you the environment to do it well:

• Structured when you want it
• Flexible when you need it
• Embedded in your existing workflows

It’s not just AI for the sake of it — it’s AI built for competitive work. By mastering prompt engineering techniques and leveraging tools like Sparks, you can unlock the full potential of AI in your competitive intelligence efforts.

Want to see what AI-powered compete looks like in action? Jump into a quick Crayon tour.