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Coach reps on their real weak spots, backed by Chorus scorecards
For sales managers who want a rep's scorecards, reviewer comments and past coaching pulled together before a session, by asking their AI assistant.
On this page
Connector
Connect Chorus, pick your AI tool and paste the prompt.
The problem
Coaching sessions repeat old points or miss the weak spot
Preparing a coaching session means pulling a rep's Chorus scorecards, reading question level scores and comments, and checking feedback they already got. Without that, coaching repeats old points or misses the real weak spot.
Without PopdockAI
- Pull the rep's scorecards for the period
- Read each question's score and comment
- Look back at earlier coaching moments
- Guess at the trend from a small sample
With PopdockAI
- Scorecards, question scores and comments are read
- Past coaching moments are checked for what landed
- Scorecard types are kept apart and small samples flagged
- One focus area is suggested and nothing is changed
The prompt
Use this prompt
Copy it into any AI tool connected to PopdockAI. Swap the values in braces for your own.
**Objective:** Build a coaching review for `{rep-name}` covering the last `{lookback-days}` days (default 90): what they have been scored on, where the scores are weak, and what has already been said to them.
**Step 1: Resolve the person**
Call `chorus-get-users` with `name.contains` set to `{rep-name}`. If more than one active user matches, list name, email, title and role and stop, asking which. Keep the exact `name` and the `email`, since the scorecard tools match on the full name and the conversation tools match on the email address.
**Step 2: Read the scorecards**
Call `chorus-get-scorecards` with `recipient-name` set to the exact name from step 1 and `submitted.from` set to the start of the window. Note `initiative-name`, `reviewer-name`, `submitted` and `scorecard-id`. If there are none, say so plainly, since an unreviewed rep is the finding.
**Step 3: Drill into the scores**
For each scorecard, call `chorus-get-scorecard-scores` with `scorecard-id` set to that scorecard's `scorecard-id`. Collect `question`, `score` and `comment`. Then work out, across all the scorecards together, which questions score lowest on average and which ones move over time.
**Step 4: Read the coaching already given**
Call `chorus-get-moments` with `recipient-name` set to the exact name and `submitted.from` set to the start of the window. These are the coaching moments already recorded. Note `initiative-name`, `reviewer-name`, `description` and `submitted`.
**Step 5: Ground it in activity**
Call `chorus-get-engagements` with `user-email` set to the email from step 1 and `date.from` set to the start of the window, to see how many conversations the scores are drawn from and how long they run.
**Step 6: Report**
Produce:
* **Summary** - how many conversations, how many scorecards, average score, and the direction of travel.
* **Strengths** - the two or three highest scoring questions, with a reviewer comment quoted for each.
* **Development areas** - the two or three lowest scoring questions, with the comments quoted.
* **Coaching already given** - what has been raised before, and whether the scores since suggest it landed.
* **Recommended focus** - one area to work on next, and why.
**Rules & Guidelines:**
* This is read only. Do not call `chorus-add-moment`, `chorus-add-playlist-moment`, `chorus-create-playlist`, `chorus-update-playlist`, `chorus-update-playlist-moment`, `chorus-update-moment`, `chorus-export-recordings` or `chorus-export-scorecards`.
* Do not average across different scorecard types as though they were one scale. Group by `scorecard-type` first and say so if they differ.
* Do not infer intent from a low score. Quote the reviewer comment or say there was none.
**Validation:**
* If no user matches `{rep-name}`, say so and suggest searching by part of the name.
* If the user exists but has no scorecards in the window, report the conversation activity alone and flag the review gap.
* If fewer than three scorecards exist, say the sample is too small to read a trend from.Who it's for
One answer, three teams that use it
Sales managers
Walk into one to ones prepared
Strengths, weak areas and one focus come back with reviewer comments quoted as evidence.
Prepare a coaching review for Priya Shah from Chorus scorecards in the last 60 days.
Sales enablement
See whether coaching landed
What was raised before is checked against recent scores, so you know if feedback stuck.
Has the feedback we gave Sam Patel on discovery in Chorus improved his scores this quarter?
Team leads
Coach new reps on evidence
Small samples are flagged, so you do not read a trend into two calls.
Review Chorus scorecards for our new rep Alex Morgan since they started.
How it works
What PopdockAI does when you ask
You name the rep and your AI assistant prepares a coaching review from Chorus. It counts the conversations behind the scores so you know how solid the picture is.
01
Ask
You name the rep and how far back to look.
02
Read
It reads their scorecards, each question's score and comment, and past coaching.
03
Ground
It counts the conversations behind the scores and keeps scorecard types apart.
04
Report
You get strengths, development areas, what landed and one area to focus on.
What PopdockAI provides
The tools behind the answer
Ready made tools for Chorus and one secure connection to your AI tool. No code, no exports.
chorus-get-users
chorus-get-scorecards
chorus-get-scorecard-scores
chorus-get-moments
chorus-get-engagements
This report is built from Get data tools only. PopdockAI applies the row, field and parameter rules your admin sets, and every query is written to the audit trail.
The outcome
Coaching that targets the real weak spot
Related use cases
More ways to put this data to work
One prompt template with infinite possibilities.
Your AI assistant adapts its approach, tone, and capabilities to match each unique context while you maintain a single, manageable workflow.
Join forward-thinking companies that are transforming their operations with intelligent, secure AI automation.

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