CAT 
In this post, we explore a solution that uses the vector engine ChromaDB and Meta Llama 3, a publicly available foundation model hosted on SageMaker JumpStart, for a Text-to-SQL use case. We shared a brief history of Meta Llama 3, best practices for prompt engineering with Meta Llama 3 models, and an architecture pattern using few-shot prompting and RAG to extract the relevant schemas stored as vectors in ChromaDB.
Originally appeared here:
Best practices for prompt engineering with Meta Llama 3 for Text-to-SQL use cases
