How AI Turned Our Development Process into a 70% Automated Workflow

AI‑Driven Development Collaboration

In late 2024 the Zhihu B‑side product team began experimenting with Cursor AI. After an initial setback, a model upgrade in early 2025 enabled a shift from treating AI as an external expert to integrating it as an internal team member, eventually covering 60‑70% of development tasks.

Core Achievements

Development efficiency gains of 100‑200% for typical scenarios.

Three‑fold acceleration of technology exploration and prototype validation.

AI‑assisted workflow adopted in 60‑70% of tasks, creating a new human‑AI collaboration pattern.

Success Cases

Key successes include a full‑cycle Circle‑operation platform, a podcast backend project that cut development time by 57%, and a batch poster generator that reduced effort from three person‑days to one.

Failure and Pitfalls

The team identified several “cost‑vs‑benefit” traps: small‑feature development often required excessive context‑introduction time, and complex cross‑system debugging remained inefficient for AI.

Methodology and Knowledge Management

To avoid repeated introductions, the team built a .cursor/rule knowledge base that captures project architecture, coding standards, common patterns, and business rules. After each task they ran /Generate Cursor Rule to update the knowledge store, turning AI from a one‑off consultant into a lasting project expert.

Knowledge modules include:

Project context (architecture, tech stack, version).

Coding standards (error handling, API design, reuse patterns).

Business logic templates (permission checks, CRUD patterns).

Organizational Impact

Team skills shifted from pure coding to AI‑collaboration, scenario identification, and knowledge‑base maintenance. A multi‑project workspace in Cursor 0.5.x allowed cross‑project knowledge reuse, further reducing onboarding time.

Future Outlook

Upcoming trends highlighted are deeper IDE integration, multi‑project AI workspaces, and the need for security, explainability, and standardization as AI models become more capable.

Conclusions

The key insight is moving from “expert outsourcing” to “team internalization”: by continuously feeding AI with structured knowledge, teams achieve sustainable productivity gains and create a “plant‑tree, later‑shade” effect for future developers.