8 Designing solutions with large language models
This chapter covers
- Using retrieval augmented generation to reduce errors
- How LLMs can supervise humans to mitigate automation bias
- Enabling classic machine learning tools with embeddings
- Ways to present LLMs that are mutually beneficial to companies and users
By now you should have a strong understanding of LLMs and their capabilities. They produce text that is very similar to human text because they are trained on hundreds of millions of human text documents. The content they produce is valuable but also subject to errors. And, as you know, you can mitigate these errors by incorporating domain knowledge or tools like parsers for computer source code.
Now you are ready to design a solution using an LLM. How do you consider everything we have discussed thus far and convert it into an effective implementation plan? This chapter will walk you through the process, trade-offs, and considerations in designing that plan. To do so, we will use a running example that we can all relate to: contacting tech support when help is needed.