• Retrieval-Augmented Generation:
  • Fine Tuning:
    • Pros:
      • Higher quality answers with respect to prompt engineering.
      • Smaller input size during inference, since we don’t need to include the context and additional instructions in the prompt.
    • Cons:
      • It can be expensive
      • The number of parameters of the model may not be sufficient to capture the entire knowledge we want to teach to it.
      • Fine tuning is not additive, meaning it may be replacing some already existing knowledge with new information.
  • Prompt Engineering: