Write Better MRs Faster

Short answer: click any GitLab text field, press ⌃+⌥+R (Mac) or Ctrl+Alt+R (Windows/Linux), speak, then press again. AICHE inserts cleaned text at the cursor.

Where GitLab Teams Actually Lose Time

GitLab is excellent at pipeline visibility and release governance. Teams still lose time when merge requests, issues, and review threads do not carry enough context for quick decisions.

The biggest text bottlenecks are:

• merge request descriptions with deployment risk
• issue templates with reproducible env details
• inline review threads that need reasoning, not yes/no
• incident runbooks in project wiki pages

GitLab-Native Workflows

Merge requests with pipeline-aware context

GitLab reviewers look at MR description plus pipeline status. Dictate both code and release context:
"Moves order-tax calculation to service layer. No DB migration. Feature flag tax_v2 defaults off. Risk is mismatch for legacy carts created before 2025-12. Validation includes unit tests, contract tests, and staging smoke with seed account."

This helps reviewers interpret failed jobs and approve with confidence.

Issue templates for self-managed environments

Many GitLab teams run self-managed instances and need environment specifics in issues. Dictate:
"Reproduced on self-managed 17.2, runner docker-autoscale, Kubernetes executor. Failure appears only on arm64 runner group. Expected artifact upload after job build-assets."

This is GitLab-specific debugging context that generic issue text misses.

Inline review comments tied to diff hunks

In MR discussion threads, one-line comments create churn. Dictate complete rationale:
"This retry loop currently catches all exceptions and retries even validation failures. Suggest narrowing to transient network errors only. Otherwise we hide schema regressions and extend pipeline time."

Fewer back-and-forth replies, faster merge.

Wiki runbooks linked to environments and pipelines

GitLab wiki pages are often used for deploy/rollback. Dictate concrete steps:
"If production deploy fails after migrate-db, run rollback job rollback_schema first, then redeploy image tag from previous successful pipeline ID. Do not run full restore unless migration touched customer billing tables."

This creates usable ops docs, not vague instructions.

AI Inputs In GitLab

For teams using GitLab Duo prompt surfaces, AICHE is useful in:

• merge request explain/refactor prompts
• issue draft/summary prompts
• follow-up prompts for test generation or review checks

AICHE inserts prompt text. GitLab AI features execute generation.

How It Works

1. Open an MR, issue, discussion thread, or wiki page in GitLab.
2. Focus the target field.
3. Press ⌃+⌥+R / Ctrl+Alt+R.
4. Speak complete context including environment, risk, and test/deploy intent.
5. Press again to insert.
6. Submit in GitLab.

Audio is streamed for cloud transcription, processed, and discarded immediately after processing, within 1 second. No persistent audio copy.

FAQ

Does AICHE interact with pipelines, approvals, or merge rules?
No. It inserts text only.

Works with self-managed GitLab?
Yes. Same browser text-field workflow.

Can this help incident communication in MRs?
Yes. Dictated MR notes and wiki updates are usually much clearer during incidents.

Try it now: open your next MR and dictate summary, rollout risk, and test plan in one pass before requesting review.