Progressive refinement

First drafts from large language models are structurally correct but may miss nuance, tone, or domain-specific phrasing. That is by design. Glossia treats content generation the same way software teams treat code: ship a working version, review it, and improve iteratively.

The refinement loop

1. Draft: Glossia generates a structurally valid first pass based on your source files and the context in L10N.md.
2. Review: Your team flags issues through pull requests and diffs, the same workflow you already use for code.
3. Refine: Updated context files, glossary corrections, and review feedback feed into the next run.
4. Converge: Each cycle narrows the distance to production quality. The system learns your product's voice through the context you provide.

Why this works

The key insight is that context accumulates. Every review comment that leads to an updated L10N.md or a corrected glossary entry improves all future runs, not just the file that triggered the review.

This follows the same principle behind Kaizen in manufacturing and successive approximation in engineering: start with a good-enough baseline and systematically improve it with human judgment in the loop.

Practical implications

• Do not expect perfection on the first run. Plan for one or two review cycles.
• Invest time in writing clear context files. They are the highest-leverage improvement you can make.
• Use the server translation session to track which files were translated, skipped, or failed.