GenAI as Force Multiplier
How LLMs accelerated delivery while architecture, judgment, and accountability stayed human-owned.
I used GenAI to move faster and explore more options—not to outsource thinking. The work still required systems decomposition, critical judgment, and iteration when early ideas didn’t work.
Design acceleration
Prompt engineering
Iteration loops
Quality gates
Human-owned decisions
Positioning (plain English):
GenAI improved throughput and creativity. I owned architecture, trade-offs, validation, and the final output.
Where GenAI Helped Most
- Decomposition: mapping a vague goal into buildable modules and interfaces.
- Prompted scoring design: turning “signal ideas” into explicit dimension definitions and output contracts.
- Implementation acceleration: drafting code skeletons, edge-case handling, refactors.
- Debugging: fast hypothesis generation for failures and bad outputs.
- Documentation: converting decisions into crisp write-ups for repeatability.
What GenAI Did Not Replace
- Architecture ownership: data flow, boundaries, extensibility decisions.
- Trade-offs: MVP scope vs accuracy vs compute vs time.
- Validation judgment: what counts as “signal” vs noise vs artifact.
- Interpretability choices: scoring and visualization that don’t overclaim.
- Iteration discipline: knowing when to redo work instead of polishing the wrong thing.
How I Kept It Reliable
- Output contracts: prompts required strict JSON only.
- Clear definitions: each dimension had a measurable meaning, not vibes.
- Repeatable prompts: reusable templates rather than one-off chatting.
- Human review gates: spot checks and sanity checks before scaling runs.
- Versioned iteration: prompts evolve like code: changed deliberately, tested, kept or rolled back.
Concrete Example
See the exact prompt used to score text across nine narrative dimensions: