LLM Eval For Text2SQL

Ankur from Braintrust discusses the systematic evaluation and enhancement of text-to-SQL models. Highlighting key components like data preparation and scoring mechanisms, Ankur demonstrates their application with the NBA dataset. The presentation emphasizes iterative refinement through advanced scoring and model-generated data, offering insights into practical AI evaluation pipelines.

00:00 Introduction
Ankur introduces Braintrust, highlighting their team, history, and industry connections.

02:11 Purpose of Evaluations
Evaluations determine whether changes improve or worsen the system, facilitating systematic enhancements without regressions by continually assessing performance and analyzing outcomes.

03:40 Components of Evaluation
Ankur outlines three crucial components: Data (initially hardcoded for simplicity), Task function (transforms input into output), and Scoring functions (from simple scripts to intricate heuristics). Issues in evaluations are often resolved by adjusting these components.

07:40 Demonstration
Ankur presents the NBA dataset for the text-to-SQL task.

08:33 Simple text2sql Function
Ankur walks through the text2sql task function using the Braintrust OpenAI wrapper.

11:58 Data and Scoring Functions
The evaluation process for SQL query generation begins with five questions, bootstrapping a dataset through human review, error correction, and creating a "golden dataset." Binary scoring simplifies query correctness evaluation.

13:16 Braintrust Project Dashboard Overview
Ankur showcases the Braintrust project dashboard, enabling prompt tweaking, model experimentation, and query saving for task refinement.

17:03 Revisiting the Evaluation Notebook with New Data
Using a new dataset with answers and queries, Ankur introduces the autoevals library for advanced scoring functions, enhancing evaluation.

20:08 Results with New Scoring Functions and Data
Ankur demonstrates improvements with updated functions and data, detailing how the scoring functions were applied.

24:33 Generating New Data Using Models
Models generate synthetic data for new datasets, validating SQL commands and questions before dataset inclusion.

28:36 Task Evaluation with Synthetic Data
The dashboard compares results across datasets; no improvements were observed in this instance.

31:30 Using GPT-4 with New Data
Results declined across all datasets using GPT-4 compared to GPT-4o.

33:45 Real-World Applications of the Evaluation Pipeline
Hamel discusses practical applications of similar pipelines and the added value of tools like Braintrust.

35:18 Other Scoring Functions
Ankur discusses various scoring functions for SQL and RAG tasks, emphasizing Braintrust's evaluation tools and workflows.

38:22 Comparison with Langsmith
Both platforms offer unique UIs and workflows; choosing between them requires trial and evaluation.

39:10 Open-Source Models on Braintrust
Braintrust supports open-source models, though some lack tracing features found in OpenAI and compatible APIs.

43:04 Use Cases Where Braintrust Pipeline is Not Ideal
Braintrust focuses on inspecting individual examples, less suited for use cases with extensive datasets.

47:22 Navigating Complex Databases
Guidance on handling text-to-SQL for large databases includes question categorization and schema optimizations.