Curated metadata and retrieval-augmented generation (RAG) solve different grounding problems for SQL agents. Metadata gives the agent reviewed meaning about tables, columns, relationships, and business rules; RAG selects useful context at request time. A dependable design usually uses both, while keeping SQL generation and execution controls as separate concerns.
What each knowledge layer does
A database schema tells an agent that a table has a column named status of a particular type. It may not tell the agent which statuses count as active, whether test accounts should be excluded, or what the business means by “customer.” Those meanings need to be documented or supplied from another trustworthy source.
Curated metadata is that reviewed, relatively stable context. RAG is a retrieval method: it searches a collection at query time and supplies selected material to the model. One describes and governs knowledge; the other chooses which available knowledge is relevant to a particular request.
| Design question | Curated metadata | RAG at query time |
|---|---|---|
| What it holds | Reviewed descriptions, business definitions, caveats, relationships, ownership or lineage, and reusable query patterns. | Searchable material such as metadata, examples, logs, or documents; some implementations index embeddings for semantic search. |
| How it is maintained | Governance and review as data meaning or ownership changes. | Ingestion, indexing, and retrieval that reflect changes in the source material. |
| How context is selected | The system identifies relevant schema objects or semantic definitions for the SQL task. | A retriever searches for context relevant to the request and returns selected results. |
| Best fit | Clarifying structured data and business rules used in filtering, joins, and aggregation. | Finding relevant context across a larger collection, especially documents and other unstructured material. |
| Human review focus | Correctness of definitions, caveats, ownership, and reusable SQL. | Source quality, ingestion coverage, indexing, and whether retrieval returns suitable material. |
These are architectural distinctions, not a head-to-head performance ranking. The cited examples describe vendor systems and patterns rather than controlled comparisons.
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What belongs in a curated catalog
Start with the schema, but do not assume names and types fully explain it. OpenAI describes adding domain-expert descriptions of tables and columns to its in-house data agent, along with lineage and historical query information. That context helps explain both what objects mean and how they relate or have been used. OpenAI says its agent retrieves relevant embedded context at query time rather than scanning raw metadata or logs: Inside OpenAI’s in-house data agent. This is an account of OpenAI’s own system, not proof that the same components suffice for every organization.
- Table and column descriptions: State the business meaning, not just the technical type or name.
- Definitions and caveats: Document terms such as “active customer,” exclusions, date conventions, and known data limitations.
- Relationships and lineage: Record how objects connect and, where available, where data comes from.
- Ownership: Identify the people or teams responsible for definitions and changes, where the organization can maintain that information.
- Representative prior queries: Preserve useful examples of how data has been queried, while treating historical usage as context rather than automatic authority.
Keep reviewed definitions close to the data objects they describe so the SQL path can use them when selecting tables, columns, filters, and joins. Metadata needs an owner and a refresh process: an outdated business rule can mislead an agent as readily as an unclear one.
What to retrieve when a request arrives
Do not send every catalog entry, log, or document to the model for every request. First identify the task and likely relevant data or semantic objects; then retrieve only the context needed to address it. RAG is useful when the available context is too large, changes over time, or includes material that cannot sensibly live in a compact schema description.
For example, a question such as “Which customers spent the most last quarter?” primarily calls for structured values and aggregation. The SQL path needs to know which customer and transaction tables apply, how spending is defined, and which date range corresponds to “last quarter.” A request about a policy’s wording instead calls for retrieving the relevant policy text. A mixed question may need both: retrieve the policy and query the structured data, then keep the answer grounded in each source.
In Google’s Cloud SQL example, source material and embeddings are stored with pgvector, similar vectors are searched, and retrieved results are sent with the prompt to the model. The documented flow is described in Cloud SQL AI overview and Integrate pgvector with Cloud SQL for PostgreSQL. Vector similarity can help locate relevant text; it does not by itself establish relational meaning, guarantee correct joins, or replace SQL.
How to divide SQL and document retrieval
Route by the kind of evidence the answer requires, not by the fact that a user phrased the request in natural language. Natural language can ask for a database calculation, a passage in a document, or a combination.
- Use a SQL-capable path when the answer depends on structured values, relationships, filtering, or aggregation. Constrain the available schema and provide relevant metadata.
- Use document retrieval when the answer depends on policies, manuals, reports, or other unstructured sources. Retrieve source material and ground the response in it.
- Use both paths when a question requires joining a structured result with documentary context. Make clear which part of the answer comes from which source.
Oracle describes an architecture combining a SQL agent with RAG for structured and unstructured analysis: Text-to-SQL AI agent. Google’s Cloud SQL guidance illustrates vector retrieval over material held in Cloud SQL. These are examples of implementation approaches, not evidence that one routing policy fits every workload.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.When reviewed SQL patterns help
For recurring question types, it may be safer to offer a reviewed, parameterized query pattern than to regenerate the entire SQL statement each time. EDB documents semantic aliases as reviewed parameterized SELECT statements that can appear in semantic search results: Semantic knowledge base.
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1Clear out junk files and repair common Windows errors2Fix the driver behind crashes, sound loss and screen glitches3Repair Windows errors before they cause bigger problemsThis is a product-specific design option, not independent evidence that aliases always outperform generated SQL. A practical hybrid is to use a reviewed pattern when a request clearly matches one and generate SQL for questions that do not, subject to the same schema constraints and execution safeguards.
Keep knowledge separate from SQL safety
Better context can help an agent choose meaningful tables and interpret fields, but it does not prove that generated SQL is correct or safe to execute. Curated definitions do not eliminate hallucinations, and RAG does not guarantee a valid query. Treat the knowledge layer, SQL generation, and execution policy as distinct parts of the system.
Use permissions and validation appropriate to the data and consequences of the task. Limit the schema available to the agent, verify generated statements before execution where needed, and control whether a request may read or modify data. The sources cited here describe knowledge and retrieval architectures; they do not establish a universal execution-control recipe or a performance advantage for one architecture.
A practical starting architecture
- Build a small, useful catalog: Include schema and types, readable descriptions, reviewed definitions and caveats, and ownership or lineage where available.
- Add representative query context: Select a manageable set of historical examples that clarify common usage; review them before treating them as reusable guidance.
- Separate structured from unstructured sources: Keep SQL-oriented metadata available to the SQL agent and index documents that need semantic retrieval.
- Retrieve selectively: For each request, identify relevant tables, semantic definitions, examples, or documents instead of passing the entire catalog and corpus.
- Provide a reviewed route for recurring questions: Where appropriate, use parameterized, approved query patterns alongside generation for unmatched requests.
- Maintain the sources: Assign responsibility for updating definitions and refreshing indexed content as underlying data and documents change.
There is no universal winner between curated metadata and RAG. The useful design is the one that supplies reviewed meaning for structured data, retrieves only relevant context at request time, and routes document-dependent questions to documentary evidence rather than asking SQL or vector search to do the other’s job.
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