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Curated metadata and retrieval-augmented generation (RAG) solve different grounding problems for SQL agents. Metadata records reviewed, durable meaning about tables and data; RAG finds relevant context when a request arrives. A dependable design often uses both—alongside a separate SQL generation and execution layer—rather than treating vector search as a substitute for database knowledge or querying.
What is the difference between curated metadata and RAG?
Curated metadata is maintained knowledge about a data environment: what tables and columns mean, how objects relate, and which business rules or caveats affect their interpretation. RAG is a runtime method for finding and supplying relevant material to a model for a particular request. Metadata is knowledge to maintain; RAG is one way to select context.
| Design question | Curated metadata | RAG |
|---|---|---|
| What it holds | Reviewed descriptions, business definitions, caveats, relationships, lineage, and representative query patterns. | Searchable source material and, in vector-based systems, embeddings that support similarity retrieval. |
| How context is found | The agent selects relevant schema objects or semantic definitions. | A retrieval step searches indexed material for context relevant to the current request. |
| How it changes | Owners review and update definitions as data and business rules change. | Sources must be ingested and indexes refreshed; retrieved results depend on the search and retrieval process. |
| Where human review matters | Business meaning, caveats, lineage, and reusable query definitions need review. | Source quality, ingestion, and whether retrieved passages are appropriate need attention. |
| Typical job | Grounding SQL generation for structured filtering, joins, and aggregation. | Finding relevant context from indexed metadata, usage examples, or unstructured documents. |
These are architectural distinctions, not a performance ranking. The cited designs do not establish that either approach is universally more accurate, faster, or cheaper.
Why schema alone may not be enough
Table and column names, types, and constraints give an agent useful structural clues, but they do not necessarily explain business intent. A column named status, for example, does not by itself establish which values count as an active customer or whether a particular report excludes test accounts. OpenAI describes adding domain-expert descriptions of tables and columns to supply context that the schema alone does not convey. Its account also includes lineage and historical query usage as complementary signals about how data is related and has been used. OpenAI, “Inside OpenAI’s in-house data agent”.
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Keep stable, reviewed meaning close to the objects it describes. A practical catalog can include:
- Readable descriptions of schemas, tables, and columns, including units and business definitions where needed.
- Known caveats, such as exclusions, time-zone assumptions, or status-value interpretation, when they materially affect an answer.
- Ownership and lineage information where it is available, so the agent has clues about relationships and data stewardship.
- A small set of representative historical queries that illustrate established usage, rather than treating every prior query as authoritative.
These definitions require upkeep by people who understand the data and business rules. OpenAI’s description is a first-party account of its own agent, not evidence that the same catalog components will be sufficient for every organization.
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What should be retrieved when a request arrives?
At request time, retrieve only the context that helps interpret and answer the question: relevant table definitions, relationships, caveats, examples, or source documents. This avoids placing all raw metadata or logs into every prompt. OpenAI describes its own system this way: “At query time, the agent pulls only the most relevant embedded context via retrieval-augmented generation (RAG) instead of scanning raw metadata or logs.” OpenAI.
That description explains a design choice, not a guarantee that retrieval will find the right item. RAG depends on what has been ingested and indexed, how material is represented, and whether the retrieved context is relevant to the request. Keep reviewed definitions authoritative, and use runtime retrieval to select useful context—not to silently replace governance.
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When should an SQL agent use SQL, documents, or both?
Route according to the kind of evidence the question needs. A request such as “Which customers spent the most last quarter?” is an example of a natural-language question that can be translated into structured queries; EDB uses it to illustrate text-to-SQL. A question about the meaning of a policy, a contract clause, or a procedure instead depends on unstructured source material. Some requests need both the structured result and a document-grounded explanation.
| Question depends on… | Use this path | What grounds the answer |
|---|---|---|
| Values and relationships in structured tables | SQL-capable agent using a constrained schema | Schema, curated descriptions, business caveats, and relevant semantic definitions. |
| Information expressed in documents or other unstructured sources | Document retrieval, often using RAG | Retrieved source passages or records, with the answer tied to those materials. |
| Both table values and document meaning | Combined or coordinated SQL and retrieval paths | Structured query results plus relevant retrieved documents, joined in the answer only where justified. |
Oracle describes an architecture combining a SQL agent with RAG for structured and unstructured analysis. Google’s Cloud SQL example demonstrates document-oriented vector retrieval: source material and embeddings are stored with pgvector, similar vectors are searched, and retrieved results are passed to the model with the prompt. These are implementation examples, not proof of a single routing policy or universally best design. Oracle SQL agent with RAG; Google Cloud SQL RAG example; Cloud SQL for PostgreSQL vector documentation.
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How should a SQL agent combine these layers?
- Build a useful catalog. Document schema and types, add readable descriptions and known caveats, and capture ownership or lineage where available. Select representative query examples rather than assuming raw query logs explain intent.
- Choose the relevant objects for the request. Identify the tables or semantic definitions needed for structured questions; retrieve only the supporting context necessary to interpret them.
- Use a constrained SQL path for structured answers. Generate queries against the permitted schema and metadata. RAG can help locate context, but vector similarity alone does not establish relational meaning, correct joins, or valid SQL.
- Retrieve documents for document-dependent answers. Ingest and index the source material, search for relevant passages, and provide those results as grounding context. A vector retrieval result is evidence to assess, not a substitute for the document’s meaning or the database query.
- Coordinate paths for mixed questions. When an answer requires both table values and policy or documentation, obtain structured results and document evidence through their respective paths, then make the distinction clear in the response.
- Review recurring query patterns. For questions that recur and have stable definitions, consider maintaining reviewed query templates or semantic aliases, while retaining generation for questions that do not match a reviewed pattern.
When do reviewed query aliases help?
For repeatable questions, a reviewed, parameterized query can reduce reliance on unconstrained SQL generation. EDB documents semantic aliases as parameterized SELECT statements that can appear in semantic search results. That is a product-specific implementation option; the cited documentation does not provide independent comparative evidence that aliases always outperform generated SQL. EDB Agentic AI concepts.
Aliases are most useful when a question has a well-understood definition and reusable parameters. Keep their meaning and scope reviewed, and do not force a loosely related request into a query simply because one is available. For novel questions, the agent still needs suitable schema context and a safe SQL-generation path.
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What these layers do not guarantee
- Curated metadata does not eliminate incorrect interpretations or hallucinations; it supplies reviewed context for the agent to use.
- RAG does not inherently make generated SQL correct. The documented vector examples retrieve source material; SQL generation and relational reasoning are distinct responsibilities.
- Vector similarity does not guarantee valid joins or preserve all relational semantics.
- There is no head-to-head benchmark in the cited material establishing a universal winner, accuracy gain, speed improvement, or cost reduction.
Microsoft also documents SQL and vector patterns for working with structured data and vector search, but such product documentation describes supported architecture rather than a neutral comparative evaluation. Microsoft SQL Server vector documentation.
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