Evan

Quality decisions that can survive production review, model comparison and stakeholder scrutiny. Evidence-based linguistic QA, multilingual LLM judging, pairwise preference evaluation, hallucination control, instruction-following tests, prompt-injection resistance, safety review and release-ready reporting across original Japanese, Traditional Chinese and Simplified Chinese game content.

Multilingual LQA / LLM / AI Evaluator - Self-Employed

Evidence-based linguistic QA, multilingual LLM judging, pairwise preference evaluation, hallucination control, instruction-following tests, prompt-injection resistance, safety review and release-ready reporting across original Japanese, Traditional Chinese and Simplified Chinese game content.

• Linguistic QA: meaning, omission, terminology, fluency, register, context, variables, UI fit, subtitle economy, cultural risk and functional impact
• LLM judging: correctness, relevance, instruction following, reasoning quality, style, groundedness, completeness and confidence calibration
• AI safety evaluation: hallucination, hidden-state leakage, prompt injection, unsafe transformation, policy conflict and adversarial instruction handling
• Multilingual evidence: Japanese pragmatic nuance assessed against zh-TW and zh-CN outputs without treating the two markets as script variants
• Operational reporting: searchable bug titles, reproducible steps, severity rationale, release recommendation, calibration notes and executive summaries