First Steps with Prompt Engineering
This chapter is from the book
This chapter is from the book
This chapter is from the book
In Chapter 2, we built an asymmetric semantic search system that leveraged the power of large language models (LLMs) to quickly and efficiently find relevant documents based on natural language queries using LLM-based embedding engines. The system was able to understand the meaning behind the queries and retrieve accurate results, thanks to the pre-training of the LLMs on vast amounts of text.
However, building an effective LLM-based application can require more than just plugging in a pre-trained model and retrieving results—what if we want to parse them for a better user experience? We might also want to lean on the learnings of massively large language models to help complete the loop and create a useful end-to-end LLM-based application. This is where prompt engineering comes into the picture.