Ensembles, for one. We might ask the same question of keyword extraction, for example, and then aggregate the results. Here's a horrible example, but it works a

Ensembles, for one. We might ask the same question of keyword extraction, for example, and then aggregate the results. Here's a horrible example, but it works and runs on a GPU box: https://github.com/FeatureBaseDB/Laminoid/blob/main/sloth/sloth.py https://github.com/FeatureBaseDB/Laminoid/blob/main/sloth/sl... Keyterms can be used in the prompt to drive the LLM to better grounded responses as well as helping locate relevant embeddings for RAG, when vector search isn't enough. Another consideration is writing code for processing things that look similar. For example, one might have the LLM write regex code which is then tested to work and put into production in a pipeline to parse log files, or write SQL off conversational queries, which are then run against a database.