Optimizing LLM Cascades

Let’s begin by discussing the Learning Based, Creative Prompting, and Hybrid prompt engineering framework for LLM Cascade

This method provides the model with context and teaches it through the use of examples in the learning prompt

More accurate responses can be obtained by using strategies like iterative prompting or negative prompting, which fall under the Creative Prompting category

Results from the LLM workflow prompt that incorporate enterprise data with retrieval augmented generation (RAG) will be more pertinent

Your data stays private and you avoid paying extra for compute training since RAG allows you to use your data in the LLM without retraining the model

Query concatenation minimizes pipeline cost and prefill processing by submitting many inquiries as a single LLM

By giving precise instructions, you can make sure the LLM produces outputs that are suited to your requirements