OpenAI markets ChatGPT as work data search tool, but citation reliability remains distant
OpenAI is now pitching ChatGPT as a “search engine for work data” with its new Company Knowledge feature. It feels like a pivot, from a chat-style helper to something that can actually pull bits of information out of an organization’s own files and databases. In theory, that could mean a real bump in productivity for anyone hunting down answers in a mess of PDFs, wikis or internal reports.
Still, the preview quote throws a wrench in the optimism: “LLMs are still a long way from being reliable citation engines,” which suggests the output might wobble when the model stitches together content from many open-ended sources. It also says, “it’s unclear whether ChatGPT or similar systems can handle such broad, open data sources reliably.” Getting accurate citations from several places at once is still a tough technical problem. So we’re left with a mix of bold marketing and honest admission of limits.
I can’t help wondering whether a chatbot will ever be a trustworthy work-data search tool, or if the promise is still out of reach.
LLMs are still a long way from being reliable citation engines On paper, features like this could be a real productivity boost. But in practice, it's unclear whether ChatGPT or similar systems can handle such broad, open data sources reliably. Pulling citations from multiple sources at once is technically tough and often leads to unclear or incorrect answers—a problem a recent study calls "AI workslop," which is already costing companies millions and hurting morale.
So far, large language models work best with well-defined tasks in a fixed context, or for exploratory searches that help users surface relevant sources. Every LLM-based system struggles with citations at this scale, sometimes giving inaccurate details, leaving out important information, or misinterpreting context. Research also shows that irrelevant information in long contexts can drag down model performance.
That's why context engineering - carefully selecting and structuring the information fed into the model - is becoming increasingly important.
OpenAI’s new Company Knowledge feature basically turns ChatGPT into a search box for stuff you keep in Slack, SharePoint, Google Drive or GitHub. It runs on GPT-5, so it can spit out answers that point back to the original file and even make sense of vague questions. The catch?
You have to flip the switch yourself, and it stays locked inside your own data vault - no wandering off to the public web or whipping up content from outside the set repositories. Some folks are already skeptical; large language models still stumble when you expect rock-solid citations. In theory, pulling references from a handful of internal sources should shave a lot of time off everyday tasks.
In reality, it’s still fuzzy whether ChatGPT will reliably dig up the right facts across such a mixed bag of platforms. The tech headache of stitching together and checking citations from different systems is pretty big. So, while the idea of a productivity boost is tempting, we’re left waiting to see if the reliability concerns get ironed out before anyone rolls it out company-wide.
Further Reading
- OpenAI positions ChatGPT as a search engine for work data with company knowledge - The Decoder
- Introducing ChatGPT search - OpenAI
- ChatGPT , Release Notes - OpenAI Help Center
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Common Questions Answered
How does OpenAI's Company Knowledge feature transform ChatGPT into a search engine for work data?
Company Knowledge lets ChatGPT query internal repositories like Slack, SharePoint, Google Drive, and GitHub, returning answers that reference the original documents. Powered by GPT‑5, it interprets loosely phrased queries and presents citations, aiming to boost employee productivity by surfacing relevant information quickly.
Why do experts say that large language models are still far from reliable citation engines?
Experts point out that pulling citations from multiple sources simultaneously is technically challenging and often produces vague or incorrect references. A recent study labels this shortfall as "AI workslop," which has already cost companies millions and eroded employee trust in AI-generated answers.
What limitations does the Company Knowledge feature have regarding data access and browsing capabilities?
The feature must be manually enabled and only accesses data stored within the configured corporate vault; it cannot browse the public web or generate content outside those repositories. Consequently, any information not uploaded to Slack, SharePoint, Google Drive, or GitHub remains inaccessible to the tool.
In what ways could the citation reliability issue impact the productivity boost promised by ChatGPT's internal search tool?
If citations are inaccurate or missing, employees may waste time verifying information, negating the intended efficiency gains. Misleading references can also damage decision‑making quality, leading to costly errors and reduced confidence in the AI system.