The Pivotal Shift to Prompt Engineering for ML Engineers

The key takeaway is that large language models like ChatGPT and Google's PaLM are transforming how machine learning engineers work, allowing them to shift focus from manual model building to prompt engineering. However, traditional ML techniques still have value for certain use cases. ML engineers need to upskill quickly in prompt engineering or risk falling behind.


  • Large language models (LLMs) like ChatGPT are pre-trained "foundation models" that require less manual work, so engineers can focus more on prompt engineering.
  • Prompt engineering is important to harness LLMs' capabilities but requires skill in precise wording, formatting, structuring.
  • There are four main types of prompting techniques: input-output, chain of thought, self-consistency with chain of thought, and tree of thoughts.
  • LLMs don't make traditional ML models obsolete. Each still has value for certain use cases and datasets.
  • ML engineers must upskill quickly in prompt engineering as expertise in this will become more important.


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