<ul><li>This article discusses how to measure and improve accuracy for an SQL agent built using LLM model and SQL database. Starting with a prototype, the article explores methods to measure accuracy and improve it using self-reflection and retrieval-augmented generation (RAG) techniques.</li><li>The LLM model used in this project is Llama 3.1 8B from Meta, and the SQL database is ClickHouse. After building the prototype, the author creates a “golden” evaluation set of questions and correct answers to compare the model's output with them.</li><li>The author discusses the nuances of evaluating accuracy and scoring the generated results of queries. Then, the article explores self-reflection and RAG techniques to improve accuracy.</li><li>The article also discusses the usage of Chroma database as a local vector storage with OpenAI embeddings to find chunks that are similar to the query for RAG.</li><li>Finally, after combining self-reflection and RAG approaches, the author achieved 70% accuracy, which can be further improved using fine-tuning technique.</li></ul>

From Prototype to Production: Enhancing LLM Accuracy

Discover more