Tune value points
Use this guide to adjust how value points are scored for your organization. You'll edit the strategic focus prompt, test it against real changes, save it as a new policy version, and apply it to existing reports.
- Deployment: SaaS, On-premises
- Role: Owner, Admin
Tuning changes the value points themselves. If the return read looks wrong, the lever may be your capacity assumptions rather than this page. See Set capacity assumptions.
Before you begin
To tune value points, you need all of the following:
- Owner or Admin on the organization: Editing tenant settings requires one of these roles. See Roles reference.
- An AI analysis provider connected, for full-fidelity scoring: Without one, scoring falls back to size and static signals. See Connect an AI analysis provider.
- A clear definition of what your organization values: The prompt scores business value against it, so decide this before you write.
The scoring model's internal parameters are fixed in this release and are not editable. Two controls are editable, both at Settings > General > Output Analysis.
1. Write the strategic focus prompt
The strategic focus prompt is free text, up to 600 characters, injected into the AI analysis that scores business value. It is organization-wide and effective-dated, with no work area or project override.
- Go to Settings > General > Output Analysis.
- Edit the strategic focus prompt to describe the outcomes that matter, so business value is scored against them. Clearing it disables strategic focus and removes any bias toward or away from particular work.
Describe the outcomes, not the code. The shipped default prioritizes direct customer and end-user value, defect fixes, performance, reliability, and security, and weights down routine dependency bumps and cosmetic churn. Rewrite it to match how your organization defines value, for example to signal that work on technical debt should not be penalized.
2. Dry-run the prompt before saving
Use Test prompt to run a draft against three real changes, one small, one medium, and one large, without saving anything.
Review how each change scores under the draft before you commit it. The test does not change any stored score.
No screenshot is available for this view yet. The Test prompt dialog scores three real changes — one small, one medium, one large — against your draft prompt without saving anything.
3. Decide on content analysis
The Enable content analysis toggle controls whether AI analysis reads your actual source diff:
- On: the diff is reviewed by your AI provider, producing the most accurate value, quality, and completeness scores, marked
fullfidelity. - Off: no source-diff content leaves your environment. Scoring falls back to size and static signals, marked
reducedfidelity and less accurate.
On SaaS: content analysis is not enabled by default. Contact sales to discuss enabling it for your organization.
On-premises: content analysis is off until you connect an AI analysis provider at Settings > Integrations > AI Analysis Providers. Once a provider is connected, content analysis is on by default. Turn it off at Settings > General > Output Analysis only if you have strict data-residency requirements, and expect lower scoring accuracy as a result.
Turning content analysis off does not change the scoring model. The same formula runs; the fidelity marker only records how much evidence the score rested on.
4. Save the policy as a new version
Saving applies the policy to new analysis immediately and creates a new versioned policy. The previous version is preserved, so historical scores stay traceable, and history is never deleted.
Reset to defaults appends a new version from the built-in defaults and rejoins the managed-default pool, so later default changes track automatically again.
5. Apply the change to existing reports
New scoring applies to new analysis at once. To re-score work already reported, re-run analysis one of two ways:
- From the banner that appears after saving.
- Later, from Settings > Data / Maintenance. The tab and button read Recalculate Value Points once content analysis is active, or Recalculate Output Points before then.
Both re-score retained commits against the current policy, and both reach back about 30 days, because only that much commit detail is retained. Older history keeps the scores it was given at the time. Recalculation runs your AI provider over each commit, so it is slow and spends your own provider tokens, and needs a connected provider to run at all.
Troubleshooting
A score reads as unavailable rather than a number
AI analysis has not run for that branch, so the analysis-derived scores are gated rather than shown as zero. Confirm an AI analysis provider is connected and content analysis is on, then recalculate. See Connect an AI analysis provider.
Value per dollar looks wrong after tuning
Tuning the prompt does not change what the work cost. Check your capacity assumptions at Settings > General > Cost Policies. See Set capacity assumptions.
The new prompt did not change historical numbers
Saving applies to new analysis only until you recalculate, and recalculation only reaches commits from about the last 30 days. Older scores keep the policy version they were given at the time.
If this doesn't fix it
Contact support with all of the following. Tickets missing these take longer to resolve:
- The policy version you saved and the strategic focus prompt text.
- Whether content analysis is on or off, and whether an AI provider is connected.
- The branch or report where the score looks wrong, and the value you expected.
- Your deployment kind and server version.
Related
- Value points — what each control feeds into.
- Set capacity assumptions — the capacity side of the return read.
- Connect an AI analysis provider — enabling full-fidelity scoring.