CDD Self-Assessment

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Includes 5 guided activities that help conversational AI teams adopt conversation-driven development, and build the assistants users want.

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Before you can begin to align on a conversation-driven development as a team, it’s important to measure where your team stands today. We’ve created a scoring matrix to help you benchmark your team’s current practices and identify opportunities. 

This play is designed to help your team come together on what CDD means and take a closer look at the habits and practices that are currently in place. This play can also help you prioritize which practices to work on and create a CDD roadmap for your team.


Play 1: Self Assessment

Download the CDD Scorecard

CDD Scorecard Template

60 minutes

1-10 participants
1 facilitator

Step 1: Discuss objectives

As the facilitator, set the stage for the discussion by providing background on conversation-driven development and outlining the goals for the session. Tie the discussion back to what your team hopes to accomplish with your assistant; for example, increasing your assistant’s accuracy or lowering the abandonment rate, or reducing incidents associated with deployment.

Step 2: Complete self-evaluations as a team

Give each team member a copy of the CDD Scorecard Template and allow 15-20 minutes for all participants to score the team. For each category (Conversation review, Training data, User testing, DevOps, Metrics), 1 point should be awarded for Stage 1, 2 for Stage 2, and so on. Participants should record their score for each category, total the category scores, and then divide by 5 to reach an average. Your average score corresponds to your overall CDD Stage.

CDD Scorecard

Step 3: Share the self-evaluations

Each participant reports their scores to the group and shares first impressions. Pay close attention to categories where scores varied widely among participants and discuss why opinions differed. 

Step 4: Determine next steps

Identify 1-2 areas where the team can improve their score. Brainstorm quick wins and pick one idea the team can put in place in the short term.

Discussion Questions 

  1. How does the team decide which new intents and training examples should be added?
  2. Which success criteria is the assistant evaluated against?
  3. How often do deployments result in unexpected behavior or bugs?
  4. Are there any pain points around coming up with new training examples?
  5. How much access do team members have to first-hand user research?
  6. What is the current process for reviewing conversation data? Which tools are used to sort and prioritize conversations?
  7. How much context-switching is required when reviewing conversations and taking action based on what you’ve found?

Next: Share your Assistant

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