Expert French AI Workflow QC: Task-Flagging Evaluation

Expert French AI Workflow QC: Task-Flagging EvaluationExpert French AI Workflow QC: Task-Flagging Evaluation

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Zacharie Petchokomani

pro

• Jul 5

French AI Task Flagging Quality Check

Performed French-language quality control for AI task-flagging workflows on SuperAnnotate. The work involved reviewing prompts, conversation history, model responses, and annotator decisions to determine whether tasks required special flags such as recent knowledge, expert legal/healthcare/STEM knowledge, unsafe content, external-source access, PII, language mismatch, or whether no flag applied.

As a QC reviewer, I evaluated annotator selections, approved or rejected their work, provided professional feedback, and wrote concise task-flagging rationales in English for French prompts. The project required careful judgment, guideline interpretation, multilingual comprehension, and consistency with the calibration framework.

The analytics screenshot shows completed QA production for the RLHF 2.4 Task Flagging project, including 36 total items, 28 completed items, 8 completed items passed audit, and 3h 27m of QA work.

Vibe Coder Machine Learning Engineering C#Java Jira Quality Assurance Engineering

Zacharie Petchokomani

pro

• Jul 5

RLHF Prompt & Response Annotation

Completed RLHF annotation work using SuperAnnotate, focused on evaluating and comparing two AI-generated responses to user prompts. The work required reviewing the full task context, including the final user prompt, conversation history, and both model responses, then assessing each response for factual correctness, reliability, completeness, reasoning quality, clarity, formatting, and overall usefulness.

For each task, I identified strengths and weaknesses for both responses, assigned quality scores, selected the stronger response, and wrote concise preference justifications aligned with RLHF evaluation standards. The work included technical and coding-related prompts across languages such as JavaScript, TypeScript, PowerShell, and C#, requiring both software engineering judgment and AI response evaluation expertise.

The analytics screenshot shows production activity for an RLHF Code Annotation project, including 48 total items, 46 completed items, 2 submitted to QC, and 38h 47m of annotation work.

Vibe Coder Machine Learning Engineering Quality Assurance Engineering C#C++Java

Vibe Media

• Jul 6

AI-assisted builder creating apps, avatars, brand worlds and interactive demos and creative AI tools. I help turn ideas into polished products fast — from concept, UX and visuals to video, automation and launch-ready experiences.