5 Essential AI Prompts for Analyst Relations Professionals

If there’s one thing that has become abundantly clear in the age of AI, it’s that your output is only as good as the input. Garbage in, garbage out. 

We previously wrote about How to Make Your AR Program Data AI-Ready. Today we’re sharing the top prompts and use cases for putting that data to work, so you can move faster, do more with less, and spend more time building quality relationships with your analysts. 

Our team of 150+ analyst relations consultants support best-in-class AR programs, day in, day out. Below is a curated collection of proven prompts aligned with core AR workflows, that you can copy, customize, and put to work right away.

1. Building a Briefing Prep Doc

A good prep doc requires time to gather all of the context. The analyst’s latest research, social posts, and insights from previous interactions. We don’t always have the luxury of time, and with this prompt, you can pull all the pieces together at once to get a head start: 

"Build a prep doc for my [DATE] briefing with [ANALYST] at [FIRM]. Include their recent coverage and POV on [CATEGORY] (cite your sources), the key themes from my last several interactions with them, recommended talking points, and any open follow-ups from prior engagements."

Our clients are cutting 30 to 45 minutes off every prep doc using prompts like this one. If your AI can pull from your interaction history directly, great; if not, paste in your notes and let it do the structuring.

2. Map the Sentiment Landscape

Before a leadership update or planning cycle, you want to know which analysts are advocates, which are skeptics, and how confident you can be in each:

"Based on my interaction notes and any coverage you can access, classify the analysts covering [MY COMPANY] as Advocate, Skeptic, or Neutral/Mixed. For each, note how many data points support the classification and the main theme driving it."

Diligent note-taking and insight extraction are a prerequisite for meaningful sentiment analysis, but the upfront work you put in to capture notes and insights will enable you to quickly dissect themes across interactions when using AI.

3. Plan Your Outreach for the Offseason

The window to influence an evaluative report closes long before kick-off. Use interaction history to time your outreach:

"An evaluative report in [CATEGORY] from [FIRM] is expected around [DATE]. Based on my past interactions with the authoring analysts and the themes they've raised, recommend an engagement cadence between now and the publish date — for each analyst, a suggested interaction type, timing, and the rationale."

Your performance in an analyst evaluation is determined in the offseason, not when you receive an RFI. Begin your interaction planning early, so you can put your best foot forward during report season.  

4. Run a Weekly Competitive Round-Up

Analysts are a key source of competitive intel that benefits teams across your organization. Your intel (and program) has the greatest impact when it’s shared on a regular basis with key stakeholders. Anchor your market scan in your own coverage first, then extend it to competitors:

"Pull together a weekly round-up for [MY COMPANY] and [COMPETITORS]: our news first, then competitor moves grouped by category, then the top five takeaways for my team. Include a direct source link for every item and flag anything you couldn't confirm."

5. Cross-Reference RFIs Before Starting From Scratch

Evaluative report RFIs include hundreds of questions and require hundreds of hours to complete thoroughly. Don’t start from scratch every single year. Compare and contrast each year’s RFI to see how the analyst’s perspective evolved and which questions require the most attention.

"I'm giving you two RFIs: last year's completed response and this year's blank questionnaire. For each question in the current one, find the closest match in last year's and build a table with: current question, match status (Yes / Partial / No), the best one or two matching questions from last year, last year's answer, and where each lives in its document. For partial matches, use the closest thematic fit; if there's no relation, mark No and leave the source columns blank."

The output is a cross-comparison that tells you exactly which answers need review, updates, or net-new responses. 

How to Get Real Value From AI and Your AR Program Data

All of these prompts depend on accurate, current program data. Consistently logged interactions, structured insights, and up-to-date analyst coverage are key to high-quality AI output. The cleaner your data, the faster you can move. 

We built the Spotlight Oz Claude Connection to help our clients get the most out of their program data. If your program runs on Spotlight Oz, you can connect it to Claude and ask these questions against your live data — analysts, interactions, insights, reports, competitive coverage, and media mentions. Your prompts run against what's actually in your program, not just what you copy and paste into the context box. 

If you’re interested in learning more about Spotlight Oz or its Claude Connection, we’d love to chat. Contact Us.