Key Considerations for Implementing LLM-Generated SQL in AWS

Large Language Models (LLMs) are increasingly being used to automate database queries through “Text-to-SQL” implementations in enterprise systems. However, ensuring stable performance in production environments involves addressing several technical challenges, ranging from database selection to permission settings and schema management.

In an article published on Qiita on July 5, 2026, titled “Considerations for Enabling LLMs to Generate and Execute SQL: AWS Edition,” the author summarized key points to keep in mind when integrating LLMs with databases in AWS environments. Based on the results of testing and implementation shared during a JAWS Meetup, the article offers practical insights. This report organizes the key aspects engineers should consider when adopting Text-to-SQL systems, based on the aforementioned article.

The Two Choices for Database Selection

The choice of the backend database for Text-to-SQL is critical to the system’s overall success. In AWS environments, the primary options are Amazon Aurora (RDS) and Amazon Redshift. The choice depends largely on the nature of the generated SQL and the type of workload.

Redshift is better suited for scenarios involving analytics queries that heavily utilize GROUP BY or JOIN operations. It shines in situations demanding high aggregation performance due to its columnar storage architecture. Additionally, Redshift Serverless can completely halt operations during idle periods, minimizing costs during downtime, which is an important consideration.

On the other hand, Aurora is ideal for cases where the user wants the LLM to perform update operations like “update this record,” or when the database features a highly normalized OLTP schema with numerous tables and complex relationships. Aurora is also the preferred choice when strict adherence to standard PostgreSQL or MySQL syntax is required.

Redshift’s Dialect

Although Redshift is based on PostgreSQL, it does not support certain PostgreSQL features. When LLMs generate SQL based on general PostgreSQL knowledge, errors may occur with syntax like LATERAL JOIN or RECURSIVE CTE.

The Qiita article highlighted the importance of explicitly informing the LLM via system prompts about Redshift’s limitations, such as “We are using Redshift. LATERAL JOIN and RECURSIVE CTE are not supported.” This proactive approach reduces errors and enhances the accuracy of Text-to-SQL outputs. Ensuring the LLM is aware of database dialect nuances is a critical factor in improving Text-to-SQL precision.

The Golden Rule of Permission Settings

When granting database access permissions to LLMs, they should always be set to read-only (READ ONLY). The article emphasizes this point as a “golden rule.”

Although LLMs generate SQL based on instructions, hallucinations could lead them to execute commands like DROP TABLE. Similarly, prompt injections could result in unintended DELETE commands. Real-world incidents, such as an AI agent autonomously executing ransomware attacks, underscore the risks of granting write permissions to AI systems.

For Redshift, a separate read-only user should be created, with SELECT permissions granted for all tables. When using the Data API, specifying the DbUser parameter in the execute_statement function ensures that SQL queries are executed as the read-only user. If DbUser is omitted, the query defaults to execution under the administrator permissions tied to the IAM role, posing a risk of undesired DELETE or DROP commands from LLM-generated SQL.

Similarly, for Aurora (PostgreSQL), a read-only role and user should be created, with credentials stored in Secrets Manager and specified via secretArn. The same approach applies to Aurora (MySQL), with SELECT-only permissions granted to the read-only user.

Providing Schema Information

To generate accurate SQL queries, LLMs require information about tables and columns (schema). The method of providing this schema data significantly impacts the accuracy of Text-to-SQL implementations.

Two primary methods can be used: full schema injection and schema linking. Full schema injection is simpler to implement and avoids mistakes in table selection. However, it consumes a large number of tokens when the number of tables is high. Schema linking conserves tokens but carries the risk of errors in table selection, which could result in incorrect SQL generation.

If the database contains approximately 20 tables or fewer, including the full schema in the prompt often yields higher accuracy. This approach aligns with findings from the BIRD benchmark, a standard benchmark for Text-to-SQL accuracy. For systems with a large number of tables and complex schemas, implementing schema linking becomes essential.

Countermeasures Against Prompt Injection

Prompt injection is a critical security concern in systems where LLMs generate SQL. Malicious users could embed SQL injection statements into natural language queries, causing the LLM to produce harmful SQL.

Setting up a read-only user is the last line of defense against such risks. Even if the LLM generates dangerous SQL, the lack of write permissions on the database ensures that the damage is limited to potential data leaks—preventing data destruction or tampering.

Additionally, placing a validation layer to check the syntax and verify against a whitelist before executing LLM-generated SQL can serve as an effective additional safeguard.

Cost Management Considerations

In Text-to-SQL systems, the size of the schema information included in prompts directly affects costs. In large-scale databases with dozens to hundreds of tables, including the entire schema in every prompt can lead to significant token consumption.

Implementing schema linking involves a trade-off between optimizing token usage and maintaining query accuracy. To enhance search accuracy, it is necessary to enrich the metadata for tables and columns and establish an efficient search infrastructure, such as by implementing vector similarity search. As token costs become a more prominent issue in AI agent operations, these design considerations will become increasingly critical in real-world applications.

Editorial Opinion

Text-to-SQL implementations are emerging as a promising approach to integrating AI with existing database assets. However, as this article highlights, there are multiple aspects to consider, from database selection to permission settings and schema management. Enforcing read-only user permissions is a fundamental safety measure when delegating database operations to AI agents, and failing to do so could result in severe risks.

Over the next three to six months, the adoption of Text-to-SQL in AWS environments is expected to rise, but this is likely to be accompanied by an increase in incidents stemming from improper permission settings. Promoting security best practices will be essential moving forward.

In the long term, advancements in LLM technology—particularly in understanding database schema autonomously and generating optimal queries—are expected to accelerate the democratization of data analysis. However, addressing LLM hallucination and enhancing resistance to prompt injection will be critical. In the span of one to three years, the focus will likely shift to improving schema search accuracy and optimizing token costs, marking these areas as key competitive battlegrounds.

References

• Considerations for Enabling LLMs to Generate and Execute SQL: AWS Edition - Qiita — Published on July 5, 2026
• BIRD-Bench (Text-to-SQL Benchmark) — Official Website