Zaon Prompt System Refactor

Overview

What was built and why it matters.

The project introduced a shared prompt contract and dependency-aware workflow for Zaon's production prompt system.

It matters because prompt quality drift and inconsistent structure can break downstream workflows as AI systems scale.

Problem

Prompt sprawl created reliability risk.

As the library grew, duplicated logic and inconsistent format increased output variance and made updates fragile.

Teams lacked clear safeguards for upstream prompt changes that affected dependent flows.

Approach

Prioritize high-impact flows and standardize contracts.

1. Audit and triage: Rank prompts by reuse frequency and regression risk.
2. Contract design: Enforce consistent sections for objective, inputs, outputs, and constraints.
3. Variable-driven prompting: Require explicit data inputs to reduce generic or hallucinated outputs.
4. Dependency checks: Validate upstream changes against affected downstream prompts before release.

Contributions

What I personally did.

• Defined prioritization criteria for prompt refactor sequencing.
• Helped author standardized prompt templates and contract expectations.
• Framed quality and regression checks for safer iterative updates.
• Documented approach and results for stakeholder communication.

Outcomes

What changed.

• Consistency: Prompt structure became more uniform and easier to maintain.
• Reliability: Regression risk decreased for high-impact production chains.
• Scalability: Teams gained a clearer foundation for expanding prompt workflows.

Lessons and Next Steps

What I learned and what comes next.

Prompt systems benefit from product-style lifecycle management: contracts, testing, release gates, and traceable change history.

Next steps would include automated prompt-level regression suites and stronger monitoring for post-release drift.

Artifacts

Model Evaluation

Model Eval: evaluation approach for quality checks and release gating.