Dust agents should answer spreadsheet questions with Table Query SQL, not semantic search

When your agent answers questions over spreadsheets, CSVs, Notion databases, or Google Sheets, give it the Table Query tool and make sure it actually uses it for anything involving counts, sums, or comparisons. Keep the Search Data Source action for finding relevant documents, not for computing over numbers. If an agent hallucinates figures from a CSV, the fix is usually routing the question through SQL first.

Context: Official Dust docs on LLM limitations document when search-based retrieval gives wrong answers on structured data. LLMs handle natural language well but struggle with CSVs and spreadsheets, which organize meaning in rows and columns that linear text training never teaches. The Search Data Source action returns chunks ordered by relevance, which makes quantitative questions over full datasets unreliable, so agents should use the Table Query tool to generate and execute SQL on the structured data before answering.

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Source: Published skill Original query: "Dust agents should answer spreadsheet questions with Table Query SQL, not semantic search" Key terms: agents, answer, dust, query, questions, search, semantic, should, spreadsheet, table

Source: Official Dust docs on LLM limitations document when search-based retrieval gives wrong answers on structured data. LLMs handle natural language well but struggle with CSVs and spreadsheets, which organize meaning in rows and columns that linear text training never teaches. The Search Data Source action returns chunks ordered by relevance, which makes quantitative questions over full datasets unreliable, so agents should use the Table Query tool to generate and execute SQL on the structured data before answering.

Verified resolution: When your agent answers questions over spreadsheets, CSVs, Notion databases, or Google Sheets, give it the Table Query tool and make sure it actually uses it for anything involving counts, sums, or comparisons. Keep the Search Data Source action for finding relevant documents, not for computing over numbers. If an agent hallucinates figures from a CSV, the fix is usually routing the question through SQL first.

Source URL: https://docs.dust.tt/docs/understanding-llm-limitations-counting-and-parsing-structured-data