Stop waiting for perfect. Ship smaller. Treat every micro-change—a prompt tweak, dataset fix, or config nudge—as a purposeful “bump” that compounds into outsized AI wins.
Example of the mindset: Simon Willison’s concise notes culture—like this short post: “bump”. Tiny updates keep momentum, surface feedback faster, and reduce breakage.
The data backs it up: smaller batch sizes and faster feedback loops correlate with better software outcomes. See DORA’s research on high-performing teams: dora.dev/research.
Why small bumps beat big releases
- Less risk: Narrow changes make regressions obvious and reversible.
- Faster learning: Each bump is a measurable experiment (did quality or cost improve?).
- Continuous story: Stakeholders see steady progress, not long silences.
Make “bump-driven” AI development work
- Slice the work: Ship prompt edits, retrieval tweaks, guardrail rules, or a single evaluation scenario—one at a time.
- Version visibly: Use a micro-changelog (e.g., v0.0.x) and note intent + outcome for each bump.
- Automate feedback: Every bump triggers evals (quality, latency, cost) on a stable test set.
- Guardrails first: Add safety checks and prompt-injection tests alongside each change.
- Share notes: Keep public or internal “notes” posts to narrate progress and spark feedback—see the bump example.
Quick start checklist
- Define 3–5 evaluation tasks that mirror real user jobs-to-be-done.
- Wire CI to run evals on every PR and write results to a simple dashboard.
- Adopt a 24–48 hour cadence for small, reversible changes.
- Document each bump: what changed, why, and the eval delta.
- Celebrate trendlines, not hero launches.
Takeaway
Momentum beats magnitude. Ship tiny, measured bumps to your AI stack and let the compounding gains do the heavy lifting.
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