Disciplined AI Collaboration: Plan, Measure, and Ship in Small, Reliable Modules

A structured, four-stage methodology helps teams collaborate effectively with AI: configure behavior, co-plan rigorously, implement in ≤150-line modules, and iterate using benchmarks. Phase 0 establishes measurement infrastructure so every change is validated by data, not guesswork. Automated checks, a project extraction tool, and strict boundaries keep architecture consistent and code maintainable.

Key Points

• Adopt a four-stage workflow: AI configuration, collaborative planning, systematic implementation (≤150-line modules), and data-driven iteration.
• Build Phase 0 first: benchmarking, regression detection, and CI/CD gates to make all changes measurable from the start.
• Work one component per interaction with explicit checkpoints; validate, benchmark, and iterate based on metrics rather than assumptions.
• Constrain context with small files and strict architectural boundaries to reduce drift, duplication, and debugging overhead.
• Use the project extraction tool to maintain shared context, audit line limits, and verify architectural compliance across the codebase.

Sentiment

Mixed but leaning positive: many applaud disciplined guardrails, planning, and tests as the key to making LLMs effective and cite speed/scaling wins; skeptics push back on overhead, brittleness, and over-formalization.

In Agreement

• LLMs work best under tight constraints and guardrails—detailed plans, small modules, and explicit boundaries improve reliability.
• Strong planning (file-by-file change outlines) plus robust tests and red-teaming materially boost agent performance and maintainability.
• Empirical measurement and automated quality gates (benchmarks, size checks, DRY audits, CI/CD) prevent drift and code bloat.
• The 150-line-per-file constraint enforces modularity, keeps components comprehensible/testable, and scales across many modules.
• Concrete evidence suggests high development velocity is achievable (e.g., PhiCode built rapidly and scaled to 70+ modules).
• Autonomous pipelines that chain planning, implementation, and review—with multiple solution attempts and strict validation—can run for hours productively.
• LLMs can review code effectively if given fresh context and strict review instructions, with some models outperforming others.

Opposed

• The methodology adds more overhead than just writing the code, especially for small changes; it feels like a waste of time.
• It’s overkill and premature to formalize a single, strict way to use AI for coding; not all tasks warrant this structure.
• Fully automated agent pipelines are risky; human review should occur continuously, not just at the end.
• LLM self-review is biased; relying on it is unsafe without strong safeguards.
• Agents are weak at planning unless aided by deep research tools, calling into question end-to-end autonomy claims.
• Regex-heavy approaches to code transformation are brittle; AST-based tooling would be more robust (concerns raised about PhiCode’s design).
• There’s a ‘halo effect’ around LLMs leading teams to lavish process and empathy on models more than on junior developers.