AI Code Review and Development: Propel Playbook
AI reviewers only add value when they are operated like production systems: clear scope, deterministic guardrails, evaluation loops, and change control. Propel ships that full operating model so you get predictable quality and minimal reviewer drag without building a prompt ops team.
Propel delivers GPT-5 reviewers with deterministic diffing, path-aware routing, eval harnesses, prompt versioning, and compliance logging. You can still customize prompts and routing while Propel handles the plumbing, dashboards, and governance out of the box.
TL;DR
- Propel routes every PR by risk and repo path so AI and humans focus on what matters.
- Deterministic diffing and static scan ingestion keep comments precise and non-duplicative.
- Prompt versioning, approvals, and audit logs ship built in for compliance teams.
- Eval harnesses and dashboards track acceptance, false positives, and latency automatically.
- Roll out in days using Propel defaults, then tune routing and prompts per team.
Quick answer: how to run AI review with Propel
- Connect your repos and pick the out-of-the-box routing rules that match your risk model.
- Propel pulls repo context, Code Owners, and static scan output automatically.
- Turn on GPT-5 reviewers with deterministic diffing so comments are reproducible.
- Use the built-in eval harness and prompt versioning to test changes before rollout.
- Monitor acceptance, false positives, and latency in Propel dashboards; adjust routing with one click.
1) Define the goal and scope
Decide what the AI reviewer owns before rollout. Good scopes: missing tests, security regressions, risky data access, performance hotspots, readability issues that block handoff. In Propel you can set this with path-based routing and severity rules instead of writing custom prompt logic.
Propel setup tip
Use Propel routing rules to map scopes to repositories and paths. Security-critical paths can require human co-review; low-risk docs can be auto-merged after an AI pass. No custom glue code required.
2) Build a gold PR corpus
Propel ships an eval harness and sample corpora so you can benchmark on day one. Import your own PRs, tag expected findings, and rerun whenever prompts or models change. Results flow to dashboards automatically.
3) Route by risk and context
- Low risk: docs and comments with no code paths. AI review can approve.
- Medium risk: leaf changes with tests. AI review plus optional human spot check for accuracy.
- High risk: auth, payments, infra changes. Require AI review, human approval, and policy checks.
Propel includes Code Owners, static analysis results, and repository metadata in the review context automatically. That keeps feedback specific and reduces churn.
4) Run prompt operations with change control
Propel versions prompts and routing templates, enforces approvals, and blocks deploys until evals are green. Every review is stamped with model and prompt version so you can trace quality shifts without extra tooling.
5) Blend deterministic checks with AI
Propel blends deterministic scans and AI findings into a single review. Static checks run first, AI consumes the results, and developers get one focused set of comments instead of duplicates.
6) Make humans and AI complementary
Propel lets you define responsibilities by path and risk so AI flags missing tests and risky patterns while humans validate impact. Onboarding stays simple because rules live in one place.
7) Instrument trust and throughput
- Acceptance rate: percent of AI comments accepted or acted on.
- False positive rate: comments dismissed as incorrect.
- Coverage: percent of PRs with AI review by repo and risk level.
- Latency: time from PR open to first AI comment and to final approval.
- Escaped defects: bugs reported after merge that AI should have caught.
Propel tracks these automatically and shares weekly digests. Improvements are visible as models and prompts evolve without spreadsheet work.
8) Add safeguards and compliance
Propel enforces data policies, masks secrets, restricts repository scopes, and logs AI access. Regulated teams get reasoning traces and model versions without extra integration. Incident playbooks are built in so reviewers know how to proceed.
9) Run a careful rollout
- Pilot with one squad and narrow scope using Propel defaults.
- Share the built-in dashboards and safeguard rules so trust builds quickly.
- Train reviewers with Propel's accept and dismiss flows; collect feedback in product.
- Expand to more repositories once acceptance rates stay above 80 percent.
- Tune routing and prompts monthly with the eval harness; no custom tooling required.
FAQ
How do I keep false positives low?
Propel feeds static scan context, applies path-aware routing, and blocks prompt changes unless evals are green. Dismissals flow into dashboards so you can adjust routing instead of making the prompt longer.
Which PRs should stay human only?
High-risk changes that touch auth, payments, data egress, or incident response should always get human approval. AI can still summarize and flag risks but should not be the final gate.
What if my stack spans many languages?
Start with the top two languages by volume. Expand the corpus and routing as you gather feedback. Propel supports language-aware reviewers and lets you pin model choices by repository to keep quality consistent.
Ship AI Code Review with Propel
Propel gives you GPT-5 review agents, deterministic diffing, eval harnesses, and governance controls so every pull request gets accurate, auditable feedback.
