build-review-interface

Build a custom LLM trace review interface by creating a web-based HTML, CSS, and JavaScript frontend that loads traces and collects structured human feedback. This annotation interface streamlines data labeling and quality assessment workflows directly in the browser.

Yes, you can save human feedback and annotations locally using CSV, SQLite, or JSON file formats. The interface collects trace-level Pass/Fail decisions, notes, and deferrals, exporting the structured data for offline analysis.

Label LLM responses for quality assessment by deploying a custom annotation interface that formats data human-readably and provides binary Pass/Fail buttons alongside a notes field. This enables efficient trace analysis and qualitative data collection.

No backend server is required to collect structured feedback on AI traces. The annotation interface operates entirely with frontend HTML, CSS, and JavaScript, utilizing local file saving options like CSV, SQLite, or JSON for data persistence.

Yes, the annotation interface visually formats data human-readably, displaying emails as emails and rendering code with syntax highlighting. It also visually links related elements within the LLM traces to facilitate better trace analysis.

Handle large-scale data labeling by building a custom web interface to review LLM generated responses sequentially. Reviewers can quickly label traces as helpful or unhelpful using Pass/Fail buttons, add notes, and defer traces for later evaluation.