  Large Language Models (LLMs) have gained considerable notoriety in the field
of natural language to SQL tasks (NL2SQL). In this study, we show how task
decomposition can greatly benefit LLMs in database understanding and query
generation in order to answer human questions with an SQL query.
  We fined-tuned open source models, specifically Llama-2 and Code Llama, by
combining 2 different models each designated to focus on one of two tasks in
order to leverage each model's core competency to further increase the accuracy
of the final SQL query.
  We propose a new framework to divide the schema into chunks in order to fit
more information into a limited context. Our results are comparable with those
obtained by GPT-4 at the same time being 135 times smaller, 90 times faster and
more than 100 times cheaper than GPT-4.
