Avoiding AI Wrong Turns: A Case Study in Apache Superset

"70% of engineering time is spent figuring out what to build and how, not typing code. We tested this head-on: building a bulk export feature in Apache Superset. Cursor wrote code fast - but it missed the architecture entirely. Nuvineer produced what your best TL would: a pragmatic design doc, milestone plan, and implementation strategy. The lesson: AI coding without context drains velocity. Context-driven design scales it."

The Challenge

Engineering leaders know the tension: copilots and reasoning models can speed up coding, but without the broader system context — especially in teams facing attrition, constant onboarding, and fragmented tribal knowledge — what gets built is brittle, one-off, and hard to maintain.

Coding agents can generate pull requests in seconds, but in a mature system the hard part isn't typing code — it's making the right architectural decisions, planning milestones, and thinking through edge cases. That's the judgment senior engineers bring, and it can't be skipped. The problem: when every design decision depends on them, they become bottlenecks. What's needed is a way to scale that level of rigor across the whole team, so velocity increases instead of stalling.

Here's the reality: roughly 70% of engineering time is spent figuring out what to build and how to build it. Only about 30% is actual coding. AI agents can already accelerate the 30%. But the harder question is: how do you give them the context and decision-making ability of a senior engineer, so they can also accelerate the 70%?

That's exactly what Nuvineer addresses.

We chose a concrete, non-trivial feature request to test this — coming in with no prior experience working on Superset:

"Enable users to select multiple dashboards in Superset and export them as a single polished PDF or image file."

This is exactly the kind of feature that looks simple on the surface but requires architectural alignment to deliver at scale.

Method B: Model-Only (Baseline)

We started by asking a reasoning model inside Cursor to "add support for bulk dashboard export."

Here's what happened:

• Hallucination: It found Superset's YAML bulk export and concluded the feature already existed.
• Wrong direction: It scaffolded a CLI flag for YAML downloads - unrelated to rendered PDF/image export.
• Naïve rendering: It suggested using client-side dom-to-pdf to stitch multiple dashboards together.
• No production concerns: No thought about RBAC, concurrency, secure storage, cleanup, or monitoring.

In short: we got code that looked plausible, might demo once, but would fail in production. It solved "make something export" but ignored the system it lived in.

Method A: Nuvineer (Context-Aware)

Next, we ran the same request through Nuvineer. Instead of starting with code, Nuvineer produced a detailed design doc and milestone-driven implementation plan - the kind of artifact you'd expect a seasoned senior engineer to produce.

Highlights from the plan:

• ✅ RBAC validation: Every dashboard revalidated against user permissions before export.
• ✅ Celery orchestration: Dedicated export queue, worker scaling, retries, and monitoring.
• ✅ Server-side rendering: Reuse Superset's existing screenshot/report infrastructure for fidelity and security.
• ✅ File storage & cleanup: Store consolidated exports in a temporary directory with tokenized download links and scheduled cleanup.
• ✅ Style templates: Apply consistent branding/formatting across dashboards with configurable templates.
• ✅ Milestones: Break delivery into 5 stages - infra, UI, rendering, file/storage, and E2E/performance.
• ✅ Success metrics: Export p95 ≤30s, handle 100+ concurrent jobs, >99.5% success rate.

And here's the key: this wasn't written by a Superset maintainer. It was generated by Nuvineer itself, reasoning like a senior engineer who knows how to align features to an existing architecture.

Milestones for Delivery

Nuvineer didn't just produce architecture; it also produced a phased delivery plan:

Each milestone had clear scope, dependencies, risks, and success criteria. That's how senior engineers think production-grade.

Why This Matters to Engineering Leaders

If you lead an engineering org, you've probably seen this dynamic:

• Copilots generate working code quickly.
• But they don't negotiate system constraints like RBAC, scaling, or observability.
• Your senior engineers have to rewrite or harden those features later.

And remember: 70% of your team's time is already spent deciding what to build and how to build it. Only 30% is actual coding. Copilots can help with the 30%. Nuvineer accelerates the 70% by injecting senior-level rigor into design and planning - the part that actually unlocks velocity at scale.

The payoff:

• Features that scale the first time
• Faster onboarding for new contributors (contractors, juniors, AI agents)
• Less risk of brittle, one-off code making it to production

Side-by-Side: Naïve vs Nuvineer

Conclusion

Adding multi-dashboard export to Superset showed the difference clearly:

• Without context: a model built the wrong thing.
• With Nuvineer: the system itself produced a design and implementation plan your senior engineers would be proud of - before a single line of code was written.

Engineering leaders don't win by typing faster. They win by making the right decisions faster and by scaling senior-level rigor across the team without bottlenecking the very people who carry it. That's what Nuvineer delivers.

That's how engineering orgs unlock velocity without drowning in refactors.

If you're leading an engineering org, Nuvineer can give your entire team a turbo head start on the work that actually are time sinks: design docs, milestone plans, and architectural guardrails generated on demand.

👉 If you're leading an engineering org and want to see this in action, reach out for a pilot at onboard@nuvineer.ai.