## 3 Model Design and Implementation
### Pre-Processing  
In the NL2 SQL (Natural Language to SQL) model, the data preprocessing and prompt generation steps are key steps to ensure the model can understand and correctly generate SQL queries. Data preprocessing aims to convert natural language queries and database structure information into a format that the model can understand and process. The steps are as follows:  
* Natural language cleaning and standardization; Denoising: remove irrelevant characters, punctuation, extra spaces, etc. Standardization: unify the format of dates, numbers, etc. * Word segmentation and part-of-speech tagging; Word segmentation of natural language queries and splitting sentences into words or subwords helps the model identify keywords, entities, and operations. Part-of-speech tagging (POS Tagging) helps the model understand the grammatical role of each word. * Named Entity Recognition (NER); Entity recognition: identify entities such as table and column names in the query. * Syntactic analysis and dependency analysis; Through syntactic analysis, understand the grammatical relationship between words in the query, such as the subject-verb-object relationship. Dependency analysis helps identify the dependency relationship between words. * Database schema mapping; Obtain the database structure information, such as table names, column names, and their data types. This step is key to matching the entities in the natural language query with the actual tables and columns in the database. * Semantic understanding and constraint analysis; Identify the conditions, constraints, and goals in the query to help the model understand the business logic of the query. After completing data preprocessing, generating appropriate prompts is key to passing natural language queries and database schema information to the model. The purpose of this stage is to guide the model in generating correct SQL statements by constructing effective prompts. The steps are as follows:  
* Constructing prompt templates; In order to help the model understand the task, prompts are usually constructed using templates. Common template forms include basic templates and templates combined with database structure. * Combined with database schema information; When generating prompts, not only natural language queries but also database table structure information should be passed in so that the model understands the context of the query. * Context-enhanced prompts;Use additional contextual information to enhance prompts, especially in complex query scenarios. Guide the model in understanding the query intent. The prompt should clearly express the query's intent, such as specifying operations (SELECT, WHERE) or constraints. * Prompt optimization;  
Improve the model's performance through different prompt structures or diversified SQL query templates. * Adjustment and optimization. Through experiments and model tuning, adjust the format or content of prompts to improve the accuracy of generated SQL. For example, try to use more detailed table structure information or more specific business context to enhance the effectiveness of prompts. The data preprocessing and prompt generation algorithms are shown in Algorithms 1 and 2.