Foundations and Concepts

• Introduction to AI Translation — when to use AI vs human translators; pilot testing and acceptance criteria.
• How Neural Machine Translation Works — plain‑language overview, common failure modes, and confidence signals.
• Large Language Models and Translation — when to use LLMs for tone, rewriting, and multi‑step chains.

Practical Workflows

• Choosing the Right Tool — privacy, cost, language pair, and pilot comparison checklist.
• Prompting for Better Translations — templates, role prompts, and iterative refinement techniques.
• Post‑editing Machine Translation — two‑tier post‑edit approach, checklists, and efficiency metrics.

Content Types and Media

• Emails and Messages — templates for formal, casual, and escalation replies; PII handling.
• Documents and Forms — OCR, layout preservation, translation memories, and legal review rules.
• Voice and Speech Translation — ASR→MT→TTS pipelines, live vs recorded workflows, and timing tips.
• Images Signs and OCR — capture standards, OCR confidence handling, and layout reinsertion.

Localization and Quality

• Localization Basics — locale briefs, cultural adaptation, and formatting rules.
• Glossaries and Terminology Management — building, governing, and integrating glossaries.
• Quality Assurance and Validation — automated checks, human review, and calibration exercises.

Governance and Scale

• Maintaining Confidentiality — redaction workflows, private endpoints, and audit practices.
• Automation and Workflows — confidence thresholds, routing rules, and observability dashboards.
• Cost Licensing and Copyright — cost modeling, vendor terms, and rights management.

People and Ethics

• Accessibility and Inclusive Translation — plain language variants, captions, and assistive testing.
• Ethics Bias and Fairness — bias audits, mitigation steps, and escalation paths.
• Teaching Others and Building MT Literacy — workshops, cheat sheets, and calibration exercises.

Future Proofing

• Future Trends and Staying Current — quarterly pilot cadence, modular pipelines, and learning loops.