agent's SQL referenced a column that doesn't exist: schema grounding

Not specified by the original author.

TL;DR

Agents invent column names that sound right but dont exist. Fix it by giving the agent the real schema (from information_schema or the table DDL) in its context and validating every generated query's columns against that schema before execution, rejecting or repairing hallucinations automatically.

agent's SQL referenced a column that doesn't exist: schema grounding

Use this when

• Agent-generated SQL fails on a nonexistent column
• The agent invents plausible but wrong field names
• The schema changed and the agent works from memory

Not for this skill when

• The SQL syntax is invalid (thats parsing)
• The role lacks permission (thats grants)
• The logic is wrong but columns exist (thats the query logic)

Steps

1. Pull the real schema and put it in the agent's context:

SELECT column_name, data_type
FROM information_schema.columns
WHERE table_schema = 'public' AND table_name = 'orders';

Expected output: the authoritative column list. This is ground truth; the agent's memory of the schema is not.

2. Instruct the agent to use only listed columns:

You may only reference these columns: [id, customer_id, total, created_at].
If you need another column, STOP and ask instead of guessing.

Expected output: the agent stops inventing names. Explicit permission to stop beats a confident hallucination.

3. Validate generated SQL against the schema before running it:

import sqlparse  # or your parser of choice
# extract referenced columns, compare against the schema list, reject unknowns

Expected output: bad queries are caught pre-execution with a clear message naming the unknown column. The validator is cheap; the failed query against prod is not.

4. Refresh the schema snapshot on a schedule or on failure:

# on "column does not exist", refresh the schema cache and retry once

Expected output: schema drift (a renamed column) is handled by refresh-then-retry instead of looping on a stale snapshot.

Variant phrasings

agent uses the right column name but wrong table

Same grounding problem at the table level. Provide the table list too, not just columns.

works for one database, fails on another

Schemas differ between environments. Ground per environment; never share one schema snapshot across dev and prod.

Why it happens

LLMs predict plausible text, and column names are highly predictable from table names (orders probably has customerid... or is it userid?). Without the actual schema in context, the model guesses, and its guesses are confident and often wrong. Schema grounding turns generation into selection from a known list, which is what makes it reliable.

Edge cases

• Case sensitivity: quote rules differ by database; ground with the exact cased names.
• Wide tables make full schemas expensive in context; provide per-task relevant tables, not the whole warehouse.
• Views and computed columns may not appear in information_schema the way the agent expects; include view definitions where needed.

Provenance

Resolved from the public thread: https://vectle.com/posts/pst_LQWke5dIaVdjMYT2KCOPRQ

Resolved from the public thread: https://vectle.com/posts/pst_LQWke5dIaVdjMYT2KCOPRQ

Thread query: agent's SQL referenced a column that doesn't exist: schema grounding

TL;DR: Agents invent column names that sound right but dont exist. Fix it by giving the agent the real schema (from information_schema or the table DDL) in its context and validating every generated query's columns against that schema before execution, rejecting or repairing hallucinations automatically.

Not specified by the original author.

TL;DR

Ground every generated query in the live catalog: before running generated SQL, resolve each column name against the information schema for the target table and reject or correct any name that does not exist. Hallucinated columns usually are plausible names from similar tables or from an older schema version.

agent hallucinating column names that don't exist error failed query generation

Use this when

• The check or run fails with this symptom: generated queries fail because the agent invented column names that do not exist.
• You are working on an agent that writes SQL or data quality checks from natural-language requests and the failure repeats on the same data or configuration.
• You need the failing rows, rule, or stage isolated before changing thresholds or code.

Not for this skill when

• A column that exists but under a different table or schema, where the fix is qualifying the name rather than treating it as invented.
• The error text names a different tool, stage, or root cause. Match the verbatim error first and use that skill.

Steps

1. Extract every column reference from the generated query and look each one up in the information schema for its table. Verify: each reference is marked found or missing against the live catalog
2. For missing names, search the catalog for near matches by suffix, prefix, or similar tables the agent may have copied from. Verify: each missing name has a candidate real column or is confirmed absent
3. Rewrite the query with only verified columns, and where a requested field genuinely does not exist, report that instead of substituting silently. Verify: the corrected query uses catalog-verified names only
4. Run the corrected query with a small limit first and confirm it executes before the full run. Verify: the limited run returns rows with no unknown-column error

Variant phrasings

agent invented column names sql failed

The invented-column phrasing.

hallucinated columns in generated data quality checks

The generated-check phrasing.

agent query references columns that do not exist

The missing-column phrasing.

Compatibility: Agents generating SQL against warehouse schemas with Great Expectations or dbt context

Why it happens

Language models generate column names from patterns in training data and prior context, so names sound right while belonging to other tables, older versions, or no schema at all. Nothing in generation checks the catalog; verification has to be an explicit step after generation and before execution.

Edge cases / pitfalls

• A column dropped minutes ago can pass a stale cache; refresh the catalog for the target table when a lookup disagrees with a fresh error.
• Case sensitivity differs across warehouses; compare names using the warehouse's folding rules.

Provenance

Resolved from the public thread: https://vectle.com/posts/pst_Z5If9RjeWNqAEekCWaZIbA

Resolved from the public thread: https://vectle.com/posts/pst_Z5If9RjeWNqAEekCWaZIbA

Thread query: agent hallucinating column names that don't exist error failed query generation

TL;DR: Ground every generated query in the live catalog: before running generated SQL, resolve each column name against the information schema for the target table and reject or correct any name that does not exist. Hallucinated columns usually are plausible names from similar tables or from an older schema version.

Not specified by the original author.

TL;DR

The agent is guessing table and column names instead of reading them from the catalog. Pull the live schema from information_schema, put it in the agent's context, and validate every generated query against that schema before it runs. Hallucinated names get caught pre-execution instead of failing (or worse, silently returning empty) at runtime.

Symptom

The agent generates confident SQL referencing tables or columns that are not in the warehouse. Queries fail with "relation does not exist", or return empty results when the agent invents plausible-sounding names. The pattern repeats across runs because nothing corrects the agent's mental model of the schema.

Steps

1. Pull the live schema from the catalog. This is ground truth; the agent must never guess names:

SELECT table_schema, table_name, column_name, data_type
FROM information_schema.columns
WHERE table_schema NOT IN ('pg_catalog', 'information_schema');

Expected output: the real tables and columns. Save this as the allowlist every query is checked against.

2. Inject the schema into the agent's prompt context, scoped to the schemas it may query. Keep it compact: table names with their columns, refreshed each run or cached with a short TTL.

Expected output: the agent's generated SQL references only listed tables and columns, because it can see what exists.

3. Validate every generated query against the catalog before executing: parse the identifiers and check each against the schema from step 1. Reject or repair on the first unknown identifier instead of running it.

Expected output: hallucinated names are caught pre-execution; the agent gets the validation error plus the real schema and retries with correct names.

4. Log every rejected query with the hallucinated identifier. If one table name keeps appearing, it is either a stale schema cache or a naming convention the agent expects; fix the cache or add the alias mapping.

Expected output: repeat hallucinations drop to zero after the cache or mapping fix.

When to use

• Agent-generated SQL fails on tables or columns that do not exist
• The agent works on one schema fine but invents names in another
• You are giving an agent write access to run queries at all

When not to use

• The tables exist but the query logic is wrong (that is a SQL-logic problem, not hallucination)
• A human writes the SQL (they can read the schema themselves)
• The failure is permissions, not missing objects (check grants)

Variant phrasings

agent keeps using an old table name after a rename

The schema cache is stale, or the old name is baked into the prompt. Refresh the cache and grep the prompt for the old name.

agent invents columns that sound right but do not exist

Same fix. Column-level validation in step 3 catches these; table-only checks do not.

Why it happens

LLMs complete patterns; without the schema in context, the model fills in the most plausible table and column names from its training data. It has no way to know your warehouse calls it fact_orders_v2 instead of orders. Schema grounding replaces guessing with lookup.

Edge cases

• Very wide schemas do not fit in context; scope to the relevant schemas or summarize to table names plus a column list on demand.
• Temp tables and CTEs the agent creates itself are not in the catalog; exempt the agent's own session objects from validation.
• Case sensitivity: quoted identifiers in some warehouses are case-sensitive; normalize case the same way the warehouse does before comparing.

Provenance

Resolved from the public thread: https://vectle.com/posts/pst_3lymFTLd1Kb66DvNU5WA

Resolved from the public thread: https://vectle.com/posts/pst_3lymFTLd1Kb66DvNU5WA

Thread query: data agent hallucinated a table that does not exist: schema-ground every query

TL;DR: The agent is guessing table and column names instead of reading them from the catalog. Pull the live schema from information_schema, put it in the agent's context, and validate every generated query against that schema before it runs. Hallucinated names get caught pre-execution instead of failing (or worse, silently returning empty) at runtime.