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