Text to SQL LLM
Getting AI to create quality SQL: An explanation of text-to-SQL methods
In order to make decisions, organizations rely on quick and precise data-driven insights, and SQL is essential to their ability to access that data. Gemini allows Google to produce SQL straight from natural language, often known as text-to-SQL. This feature enables non-technical users to engage directly with the data they require while also boosting the productivity of developers and analysts.
What is Text to SQL?
A feature called “text-to-SQL” enables systems to produce SQL queries straight from normal language. Its main goal is to eliminate the requirement for writing SQL code by allowing people to access data using common language. In addition to enabling non-technical users to engage directly with the data they require, this procedure boosts the productivity of developers and analysts.
Technology at the Basis
Because of their capacity for reasoning and information synthesis, strong large language models (LLMs) like Gemini have played a crucial role in recent advances in Text-to-SQL. These models form the basis of Text-to-SQL solutions, and the Gemini family of models has a track record of producing SQL and code of excellent quality. To guarantee good SQL creation, particularly for specific dialects, various model versions or even bespoke fine-tuning may be used, depending on the particular requirement.
Availability on Google Cloud
Today’s Google Cloud products include text-to-SQL functionality:
- BigQuery Studio: accessible through the Data Canvas SQL node, SQL Editor, and SQL Generation tool.
- Cloud SQL Studio: Offers Postgres, MySQL, and SQLServer “Help me code” features.
- “Help me code” features are also included in AlloyDB Studio and Cloud Spanner Studio.
- AlloyDB AI: Presently in public preview, this tool provides a direct natural language interface to the database.
- Vertex AI: Offers direct access to the Gemini models that support these features of the product.
Challenges of Text-to-SQL
Although the most advanced LLMs available today, such as Gemini 2.5, are capable of reasoning and effectively converting intricate natural language queries into functional SQL (such as joins, filters, and aggregations), there are challenges when using Text-to-SQL against real-world databases and user queries. To solve important issues, the model must be supplemented with other techniques. Among these difficulties are:
Providing Business-Specific Context:
Like human analysts, LLMs need a great deal of “context” or expertise in order to produce accurate SQL. This context may be implicit (business case implications, semantic meaning) or explicit (schema information, pertinent columns, data samples). It is usually neither scalable nor cost-effective to rely exclusively on specialist model training (fine-tuning) for every database form and modification. Semantics and business knowledge are frequently not well documented, which makes it challenging to include them in training data. Without this context, an LLM won’t be aware of unique business rules, such as how a given cat_id value in a particular table signals that a product is a shoe.
Recognizing User Intent
Compared to SQL, natural language is less accurate. An LLM has a tendency to give an answer when the question is unclear, which might cause hallucinations, but a human analyst can ask clarifying follow-up questions. “What are the best-selling shoes?” is an example of an ambiguous question; it could suggest that the shoes are most popular by quantity or revenue, and it’s also unclear how many results are needed. Additionally, various users need different kinds of answers; a non-technical user needs an exact, correct answer, but a technical person might profit from an acceptable, nearly-perfect query. Ideally, the system should be able to guide the user, provide explanations for its decisions, and pose clarifying queries.
LLM Generation Limits:
Unconventional LLMs are excellent at creative writing and summarising, but they may find it difficult to follow exact directions and specifics, particularly when it comes to hidden SQL features. Strict attention to specifications, which can be complicated, is necessary to generate accurate SQL. Managing the numerous changes between SQL dialects is a major task. For example, BigQuery SQL uses EXTRACT(MONTH FROM timestamp_column) to extract a month from a timestamp, but MySQL uses MONTH(timestamp_column).
Text-to-SQL Methods for Overcoming Obstacles
Google Cloud is constantly improving its Text-to-SQL agents by employing a variety of strategies to raise quality and solve the aforementioned issues. These methods consist of:
Contextual learning and intelligent retrieval: To give the required background information (data, business principles, and schema). After identifying pertinent datasets, tables, and columns using indexing and retrieval techniques (such as vector search for semantic matching), more context is loaded, such as user-provided schema annotations, examples of related SQL, business rule implementations, or samples of recent queries. Using Gemini’s capability for lengthy context windows, this data is arranged into prompts that are sent to the model.
LLMs for disambiguation: To ascertain user intent by asking the system clarifying questions in response to ambiguity. Usually, this entails planning LLM calls to see whether a question can be answered with the information at hand and, if not, to produce the required follow-up questions to elucidate purpose.
SQL-aware Foundation Models: Employing powerful LLMs, such as the Gemini family, occasionally with tailored fine-tuning to guarantee excellent and dialect-specific SQL production.
Verification and Replenishment: Addressing non-determinism in the creation of LLMs. To obtain a predictable signal in the event that something important was overlooked, non-AI techniques like as query parsing or dry runs of created SQL are employed. Since models can usually fix errors when given examples and direction, this feedback is then sent back to the model for another try.
Self-Reliability:Reducing reliance on a single generation round and increasing reliability. The best query is chosen from among the candidates after several are created for the same question (perhaps using several models or methodologies). The probability of correctness is increased when several models agree.
Semantic Layer: The semantic layer serves as a link between the customer’s daily language and intricate data structures.
Query history and usage pattern analysis: aiding in the comprehension of user intent.
One method for figuring out user intent is entity resolution.
Model finetuning: This technique is occasionally used to make sure models offer adequate SQL for particular dialects.
Assessment and Quantification
Robust evaluation is essential to enhancing AI-driven capabilities. While BIRD-bench and other common academic benchmarks are helpful starting points, they might not accurately reflect workload and real-world structure. To supplement these, Google Cloud has created a set of synthetic benchmarks that encompass a wide range of SQL engines, products, dialects, and engine-specific features, such as DDL, DML, administrative requirements, and complex queries/schemas. Evaluation combines automated and human methods, such as LLM-as-a-judge, to provide cost-effective performance understanding, even on ambiguous tasks, using both offline and user metrics. Teams can rapidly test new models, prompting strategies, and other enhancements thanks to ongoing reviews.
