Part 1: Core concepts and workflow

Superpowers of generative AI

  • Context
  • Easy and flexible trainability
  • Knowledge of the world

Necessity of new workflows

  • All translation is now revision.
  • Machine translation post-editing (MTPE) assumes a static workflow imposed from above.
  • Neural machine translation (NMT) is a legacy technology.
  • Translation memory (TM) was also designed for yesterday’s workflows.
  • Today’s CAT tools aren’t ready for the future, either.
  • “State of the art” in tools
  • Legacy technologies still have a role.
  • Generative AI Iterative Translation (GAIT) leverages the superpowers of generative AI at the translator level.
  • Keep “slow” and “expensive” in perspective.
  • Fast/free NMT is cheap, indeed!
  • If you don’t yet see the potential of generative AI in your translation workflow, then stop here!

Workflow fundamentals of Generative AI Iterative Translation (GAIT)

  • Set up the anchor prompt
  • Get source text batch
  • Send to the AI
  • Get the AI output
  • Edit the AI output
  • Update the anchor prompt

Part 2: Advanced concepts and techniques

Context, tokens, and costs

Working in your CAT tool

  • What your CAT tool is still good at
  • Leveraging context in your CAT tool
  • Why pre-translating with MT is a bad idea
  • Preparing your files for the GAIT workflow
  • Working with formatting in GAIT

Other advanced techniques

  • How a project manager could use GAIT as a replacement for substandard MTPE
  • Writing and structuring your anchor prompt
  • Using GAIT with a lot of TM hits
  • Managing tokens to balance quality and cost
  • Impact of GAIT on the overall workflow