Question

A media company wants a foundation-model application that generates news summaries in the exact editorial writing style used by their senior editors. They have 5,000 high-quality example summaries written in this style, and the style rarely changes. The underlying news content changes daily and comes from a separate live feed that is already injected into the prompt at request time. The team wants the lowest possible per-request latency and does not want to inject style examples into every prompt. Which approach best meets these requirements?

Accepted Answer

Fine-tune the foundation model on the 5,000 example summaries so the editorial style is learned into the model weights.

Answer

Build a Retrieval Augmented Generation pipeline that retrieves style examples from a vector store and adds them to each prompt.

Answer

Include all 5,000 example summaries in the system prompt as few-shot examples for every request.

Answer

Use continued pre-training on the daily live news feed so the model always has the latest content.