Update README.md

README.md

@@ -27,7 +27,24 @@ This fine-tuned model builds upon the `bigcode/starcoder2-3b` base model, furthe
 
 ## Intended uses & limitations
 
-More information needed
+## Intended Uses of the Text-to-SQL Generator
+
+This Text-to-SQL generator is designed to bridge the gap between users and databases. Here are some of its key intended uses:
+
+* **Non-programmers interacting with databases:** Users who are unfamiliar with writing SQL queries can leverage this tool to ask questions about the database in natural language and get the corresponding SQL query generated. This allows them to access and analyze data without needing programming expertise.
+* **Data exploration and analysis:** Analysts or researchers can use the Text-to-SQL generator to quickly formulate queries for exploratory data analysis. It can save time by automatically generating basic SQL queries, allowing users to focus on refining their questions and interpreting the results.
+* **Automating repetitive tasks:** For tasks requiring frequent execution of similar SQL queries based on changing parameters, the Text-to-SQL generator can automate the process of generating the queries. This can improve efficiency and reduce errors.
+* **Learning SQL:** Beginners can use the Text-to-SQL generator to experiment with natural language prompts and see the corresponding SQL queries. This can be a helpful tool for understanding the relationship between natural language and SQL syntax, aiding in learning the basics of SQL.
+
+## Limitations of the Text-to-SQL Generator
+
+While this tool offers a convenient way to generate SQL queries, it's important to be aware of its limitations:
+
+* **Complexity:** The Text-to-SQL generator might struggle with highly complex queries involving advanced SQL features (e.g., joins with multiple conditions, subqueries). It's best suited for simpler queries that can be expressed in natural language.
+* **Accuracy:** The generated SQL queries might not always be perfect. The model might misinterpret the user's intent or generate syntactically incorrect queries. It's crucial to review and potentially edit the generated SQL before running it on the database.
+* **Domain-specific knowledge:** The Text-to-SQL generator might not understand the specific terminology or structure of your database. If your database schema or data contains domain-specific terms, you might need to adjust the natural language prompts to ensure accurate query generation. 
+* **Security:**  It's important to be cautious when using the Text-to-SQL generator with sensitive data.  Ensure the tool doesn't introduce security vulnerabilities by generating unintended queries or exposing sensitive information.
+
 
 ## Training and evaluation data