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Revenue Operations

Prompt Engineering for RevOps: How to Go From Good to Great

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If AI is your intern, then prompting is your management style. In this expert-packed webinar hosted by RevOps Co-op CEO Matthew Volm, Hannah Gonzalez (RevOps Leader at NoFraud) and Jake Obremski (CEO at Swyft AI) broke down how RevOps pros can move beyond basic prompts to build scalable, context-rich, outcome-driven AI workflows.

Here’s your crash course in prompt engineering for RevOps - no coding required (just vibes).

Why Prompting Matters in RevOps

Prompting isn't just a technical skill - it’s a business lever. Garbage in, garbage out applies just as much to prompts as it does to CRM data. A well-structured prompt can lower costs, increase consistency and drastically improve the accuracy of AI outputs - even on leaner models.

But the #1 challenge? Getting meaningful results, consistently.

“AI is like a very intelligent third grader - it’s brilliant, but needs a lot of direction,” said Jake Obremski.

This messaging aligns closely with our guide on Prompting Best Practices for RevOps Teams, which emphasizes that most teams don’t have a prompting problem - they have a context problem.

Key Prompting Principles for RevOps Teams

1. Add Context Like a Pro

“RevOps” means different things in different organizations - and the same goes for prompts. A vague prompt like “analyze this call” won’t cut it.

Instead, add layers of context:

  • Who you are: “As a RevOps manager at a fraud prevention company…”
  • What you want: “Summarize the call and flag key signals of buyer interest.”
  • What context matters: “Our impact is defined by chargeback rate reduction.”

If you’re using tools like Swyft AI, feed in metadata like user roles, team type, close dates and historical patterns to enrich results even further.

💡 Tip: Use job-specific phrasing (“As a RevOps Analyst…”) instead of generic language (“You are a business analyst”) to shape AI behavior.

2. Teach AI Your Business Language

NoFraud had to train their AI to interpret acronyms and jargon like “CVV” and “chargeback rate” differently across teams. Otherwise, the model would flag normal fraud ops chatter as red flags in customer conversations.\

Start by:

  • Mapping team-specific language (Sales vs. Fraud Ops)
  • Defining what “impact” means by persona or business line
  • Regularly auditing outputs for hallucinations or contradictions

“Most hallucinations are our fault,” said Hannah. “Somewhere in the prompt, we probably contradicted ourselves.”

3. Optimize for Output and Actionability

Don’t just ask AI to “summarize.” Explain what you’ll do with the output:

  • Will this inform a CRM field update?
  • Will a Slack alert be sent to a rep?
  • Is this feeding a dashboard?

Jake recommends including output format and business goal directly in the prompt. For example: “Summarize in JSON format for ingestion into Salesforce; goal is to auto-create an opportunity."

Our blog post Can ChatGPT Increase Efficiency for RevOps? shares examples of using prompts to structure executive summaries with target audience and tone specificity.

Best Practices for Building Better Prompts

Here’s your go-to checklist:

  • Start with role framing: “As a RevOps leader at a B2B SaaS company…”
  • Add metadata: Close dates, job titles, industry context, etc.
  • Include examples: Use few-shot prompting to guide output style
  • Be prescriptive: Tell the model what not to do (“Use as inspiration, not a template”)
  • Audit results: Ask the AI to explain its logic and cite sources if possible
  • Test across models: GPT-4o vs Gemini 1.5 vs Claude Opus may yield different results

“Prompt your AI to review your prompt,” joked Matt. “It’s prompts on prompts on prompts.

Hallucinations Aren’t Failures - They’re Signals

AI hallucinations aren’t always a dead-end. They’re often cues that:

  • The model misinterpreted a term or lacked sufficient guardrails
  • You’re working with incomplete or outdated data
  • The prompt contains contradictions

Best way to spot them? Ask reps. “My reps are the first to say: ‘WTF is this?’ when something’s off,” said Hannah. “That’s our fastest QA loop.”

Storing and Scaling Your Prompts

Jake recommends tools like Swift that store prompts by use case and allow teams to test them in sandbox environments. You can also:

  • Turn on memory in ChatGPT to recall old prompts
  • Store prompts by outcome type (e.g., “lead classification,” “opportunity summary”)
  • Audit prompts quarterly as models evolve

“Don’t be afraid to start over,” added Hannah. “Sometimes your prompt needs a full reset.”

Final Takeaways

This session wasn’t just about writing better prompts - it was about using AI more responsibly and effectively in revenue workflows.

Here’s what to remember:

  • Treat AI like an MBA intern: capable, but overconfident without guardrails.
  • Good prompts reduce model cost, output errors and human audit time.
  • Audit like a RevOps pro—ask your AI how it got its answer.

Looking to take your prompting skills from good to great? Start with clarity, context and continuous iteration.

Looking for More Great Content?

Check out our blog and join the RevOps Co-op community for more tactical deep-dives, expert webinars and AI-powered playbooks for RevOps.

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