Best Practices for Connecting LLMs to SQL Databases

Hooking an LLM straight up to your production SQL database is one of those ideas that sounds cool in a demo and terrifying in a real company.

Done well, you get “ask in English, get SQL + results” and a lot less back-and-forth between data folks and everyone else. Done badly, you get slow queries, wrong numbers in executive decks, or worse, accidental data leaks and write operations you never meant to allow. Enterprise NL2SQL papers and blog posts keep repeating the same warning: accuracy and safety are the main problems, not “can the model write SQL”.

Let’s walk through practical best practices for connecting LLMs to SQL in a way that’s useful, predictable, and not terrifying for your DBAs.

1. Treat the LLM as an untrusted client
First principle: the LLM is not special. It’s just another client that can send weird queries.

So architecture-wise:

• Put the LLM behind an API layer, not directly on the database connection string.

• Let your backend service:

  • Call the LLM.
  • Inspect the generated SQL.
  • Decide whether to run it, rewrite it, or reject it.

For safety and performance, hit:

• A read replica or analytics database, not the OLTP primary.
• A separate schema or database user with limited permissions.

Safety checklists for LLM agents all hammer on the same idea: limit tools, limit permissions, and assume the model will eventually do something dumb if you let it.

2. Make the LLM schema-aware (but don’t dump the whole catalog)
Most NL2SQL accuracy issues come from the model not really “knowing” your schema: table names are strange, joins are non-obvious, and column naming is inconsistent. Recent surveys put execution accuracy in the ~60–70% range even for strong models on realistic datasets.

You boost accuracy by feeding the model the right schema context:

• Provide:

  • Table names and short descriptions
  • Column names + types
  • Key relationships (PK/FK, common join paths)

• Scope the schema:

  • Only include tables relevant to the current product area or user.
  • For big warehouses, predefine “domains” (sales, support, billing) and only send one at a time.

Tools and guides across vendors (Azure, Oracle, LlamaIndex, etc.) all follow this pattern: index the schema, then give the model a filtered view based on the question.

Too much schema = confusion. Too little schema = wrong joins. Spend time getting this balance right.

3. Use a two-step reasoning pattern, not “prompt → SQL → run”
Direct “question in, SQL out, execute immediately” is fragile. Better to split it:

• Interpret the request

  • Have the model restate the question in structured form:
    • Intent (report vs lookup vs debug)
    • Entities (customer, product, region, time range)
    • Output shape (single value, table, time series)

• Generate SQL from that plan

  • Ask the model to produce SQL and a short explanation of the join logic and filters.

Chain-of-thought style prompting (even if you don’t show the thoughts to the user) consistently improves SQL generation quality in studies and production write-ups.

Implementation tip:

• Parse only the SQL part (e.g., fenced in a code block).
• Ignore anything else when executing.

That gives you something to log and debug when a query misbehaves.

4. Lock down what SQL the model is allowed to run
Don’t rely on “please don’t write DELETE statements” in the prompt. Enforce it.

Concrete rules that work well:

• Read-only DB user

  • Only SELECT allowed.
  • No INSERT/UPDATE/DELETE/MERGE, no DDL.

• Single-statement rule

  • Reject queries with multiple statements or suspicious delimiters.

• Row and time limits

  • Always add LIMIT and sane timeouts.
  • For dashboards, page the results.

• Column allow-listing

  • Exclude PII or sensitive columns at the schema layer, or expose only safe views.

Some teams go one step further and allow the LLM to call only stored procedures instead of emitting free-form SQL. That trades flexibility for strong control: the model picks a stored proc and fills in parameters, but can’t touch arbitrary tables.

Whatever you choose, implement checks in code before execution, not just in the prompt.

5. Validate and sandbox queries before hitting real data
Even with a read-only user, ugly queries can still hurt performance or return nonsense.

Good guardrails:

• Static checks

  • Parse the SQL (e.g., with your language’s SQL parser) and inspect the AST.
  • Reject:
    • Cross-database references
    • Dangerous functions
    • Huge cartesian joins

• Dry run or EXPLAIN

  • Run EXPLAIN first and reject queries with insane cost estimates or full table scans on huge tables.

• Result sanity checks

  • Enforce row count caps.
  • If the result is empty or obviously off, you can ask the model to debug/adjust the SQL instead of returning junk.

Research on constrained NL2SQL and runtime enforcement basically boils down to this: let the model propose queries, but use hard-coded constraints to keep execution safe.

For sensitive environments, consider running first against masked or synthetic data to test prompts and behavior.

6. Put a human in the loop where the blast radius is high
Not every query needs approval. But some really should.

Patterns that work:

• For ad-hoc analytics or internal reporting, you can usually auto-run reads with good guardrails.
• For actions that:
  • Affect pricing, payouts, or compliance, or
  • Touch very sensitive tables
• …show the SQL and a plain-English summary to a human for approval first.

Safety guides for LLM agents explicitly recommend human review for any high-impact actions like editing databases; querying sensitive data can fit the same pattern depending on your risk profile.

Make it easy for the reviewer: include the original question, the generated SQL, and a quick explanation of what the query does.

7. Log everything and measure accuracy over time
NL2SQL is not “solved”, especially once you move beyond academic benchmarks into messy enterprise schemas.

Treat your LLM–SQL layer as a product:

• Log:

  • User question
  • Schema context you passed in
  • Generated SQL
  • Execution plan and runtime
  • Result shape (row count, columns)

• Sample and label:

  • Regularly review a subset of interactions.
  • Mark which SQL queries:
    • Ran successfully
    • Returned correct answers
    • Needed manual fixes

• Track:

  • Execution accuracy (did the SQL run).
  • Answer accuracy (was it the right question / result).
  • Latency and cost.

This gives you a feedback loop when you change models, prompts, or schema, and lets you catch regressions early.

8. Start narrow, then widen the blast radius
The safest path is to begin with a tight use case and expand.

A nice rollout order:

1. One domain, one schema

   • e.g., just analytics on a reporting replica of your billing DB.

2. Internal users only

   • Data/BI teams who can spot nonsense quickly.

3. Gradual schema expansion

   • Add more tables and domains once you trust the behavior.

4. Broader audiences and more powerful queries

   • Only after logs and metrics show stable, predictable behavior.

You’ll learn a lot about your own data quality, naming, and join structure along the way, which often leads to better views and marts even outside the LLM use case.

Wrapping up
Connecting LLMs to SQL databases isn’t just about getting fancy demos where someone types “show me revenue by region” and a pretty chart appears. The hard part is everything around that moment: scoping schema, locking down permissions, validating what runs, and tracking whether answers are actually right.

If you treat the LLM as an untrusted client, keep it schema-aware but constrained, add hard checks around the SQL it emits, and watch behavior with real metrics, you can give people a natural-language window into your data without giving your DBA a heart attack.