A company building a natural language query tool evaluated a custom text-to-SQL solution with Langchain but concluded it would require too much effort to build and maintain.

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    "summary": "A company building a natural language query tool evaluated a custom text-to-SQL solution with Langchain but concluded it would require too much effort to build and maintain.",
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      {
        "url": "https://www.reddit.com/r/PostgreSQL/comments/1wrmstn/comment/pcdt7z3/",
        "depth": 0,
        "score": 3,
        "author": "Zestyclose-Turn-3576",
        "excerpt": "I don't have any tool ideas, but my experience has been that business users (analysts even) do not have the ability to precisely phrase a logical question that doesn't need some additional prompting and refinement to be usable for the purpose they have in mind.\n\n I'd be interested in hearing the experiences of others, but this has always struck me as being the reason why natural language querying",
        "posted_at": "2026-09-27T15:30:53.000Z",
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        "author": "CrayonUpMyNose",
        "excerpt": "business logic that isn’t obvious from column names\n\n This is true for anything and anyone, if you're just guessing, what kind of result do you expect if you don't provide the information needed to understand the data? Chucking a bunch of undocumented data over a fence isn't going to magically solve all your problems for you.\n\n A good first step is adding detailed column descriptions, registering",
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        "author": "aquisalid",
        "excerpt": "Crazy idea: teach someone SQL.",
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        "url": "https://www.reddit.com/r/PostgreSQL/comments/1wrmstn/comment/pcedar9/",
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        "author": "elevarq",
        "excerpt": "\"Our table metadata is not rich.\"\nThat is your real problem, and no SQL can solve it for you. You have to fix your documentation first. This is where AI can help you: document the application (where you have the business logic) and the database. Expect this to be a big project, but it's fundamental for future natural language querying by non-tech users.\n\n It also helps you to find (and prevent) bu",
        "posted_at": "2026-09-27T16:55:42.000Z",
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        "author": "ibraaaaaaaaaaaaaa",
        "excerpt": "There are much more things going in these fed keywords than DDLs, and speaking from perspective of someone who built something similar, the moment it spits one time not accurate number, it loses its credibility",
        "posted_at": "2026-09-27T19:17:56.000Z",
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      "[post] Tried a couple of options so far.",
      "[post] Custom option using text2sql with Langchain, which gives better control but is a lot more to build and maintain."
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    "post_date": "2026-09-27T15:17:23.000Z",
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    "post_text": "Building an app for the operations team that runs on Postgres. Business side wants to query in plain English without writing sql. Our table metadata is not rich. Tried a couple of options so far.\n\nSetup syncs our Postgres tables into Databricks and then using Genie agents to generates sql on the fly. Works well for straightforward questions but gets shaky on anything that needs multiple business logic that isn’t obvious from column names.\n\nCustom option using text2sql with Langchain, which gives better control but is a lot more to build and maintain. And we will eventually move all workloads to cloud.\n\nWhat’s better in practice? How to deal with the metadata gap?\n\nA colleague keeps talking about Metabase for this kind of self service. Is it worth considering?",
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    "post_title": "Natural language querying on top of PG for non tech users",
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