PRDs and prototypes created in seconds won't lead to products that people love to use. You need a fast feedback loop and the right toolkit to ensure what you're building delivers real value.
AI creates fast, but it's not always right.
Here's what Pendo's own product team is doing to fight slop — and how you can, too:
01 AI-generated PRDs
The artifact looks and feels complete because AI filled every section, but it's not tailored to product usage data or broader business context.
02 Surface-level agent measurement
A thumbs-up rating tells you how a user reacted to a conversation, but not if they accomplished their goal, encountered a dead end, made an unsupported request, or left more frustrated than when they started.
03 Roadmap planning that lacks proof
AI can generate a roadmap in seconds, but it doesn't automatically know which customers are at risk, which accounts are ready to expand, or which product investments are tied to retention and growth.
01 AI-generated PRDs
The artifact looks and feels complete because AI filled every section, but it's not tailored to product usage data or broader business context.
To fix this, Pendo's Lead PM, Tiffany Kitchen, follows this playbook:
- 1
Use Pendo data, like usage, feedback, replay evidence, affected segments, and adoption history, as your source material before your LLM generates your PRD.
- 2
MCP pulls real product context into the AI workflow before the PRD is written.
- 3
Product Analytics, Listen, and Session Replay ground the problem in behavior, demand, and visual proof.
- 4
The resulting PRD includes an adoption plan and proof plan instead of polished assumptions.
02 Surface-level agent measurement
A thumbs-up rating tells you how a user reacted to a conversation, but not if they accomplished their goal, encountered a dead end, made an unsupported request, or left more frustrated than when they started.
To fix this, Pendo's Principal Product Manager, Kruti Carsane, follows this playbook:
- 1
Start with Agent Analytics to understand what's actually happening inside your AI experiences. Identify the use cases driving conversations, the unsupported requests users are making, the rage prompts signaling frustration, and the points where conversations break down.
- 2
Then connect those interactions to Product Analytics to measure downstream outcomes, including task completion, feature adoption, and retention.
- 3
When an experience fails, Session Replay captures the user context before, during, and after the conversation, helping teams understand what happened rather than relying on assumptions.
The result is a complete picture of agent performance based on user behavior and business outcomes, not vanity metrics.
03 Roadmap planning that lacks proof
AI can generate a roadmap in seconds, but it doesn't automatically know which customers are at risk, which accounts are ready to expand, or which product investments are tied to retention and growth.
To fix this, Pendo's Sr. Product Marketing Manager, McKenna Seyboldt, follows this playbook:
- 1
Connect your LLM to Pendo through MCP so planning decisions are grounded in real customer and product data from the start.
- 2
Predict identifies accounts at risk of churn and accounts showing expansion potential, along with the behavioral signals behind those predictions.
- 3
Product Analytics and Listen provide additional context by surfacing feature adoption patterns, workflow usage, customer feedback, and friction points associated with those accounts.
- 4
MCP brings that context directly into your AI workflow, helping roadmap prioritization reflect measurable retention and expansion opportunities instead of generic recommendations.
The result is a roadmap connected to customer outcomes, business impact, and the accounts that matter most.
More slop worth fixing
Open a playbook to see how Pendo grounds these AI-speed habits in real user evidence, adoption strategy, and behavioral proof.
What's in the Pendo Agent Toolkit?
Feed product context into any agent so it knows who the user is, what they've done, and what they need next. All delivered through Pendo's MCP server.
Ready to stop the slop?
See how Pendo's product context, user evidence, and actions make your AI outputs trustworthy.
