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Gracenote, the content intelligence business unit of Nielsen, today released its latest report, \u201cPlot holes in AI: Why Ungrounded LLMs Can\u2019t Fix Content Discovery.\u201d The research examines how accurately a leading large language model (LLM) answered questions about popular movies and TV shows across 2,600 titles in 13 countries. By comparing responses based only on training data with those grounded in Gracenote content intelligence, the study found that the ungrounded LLM hallucinated all measured metadata for 506 titles, or nearly one in five.<\/span><\/p>\n The report comes as streaming services and other entertainment providers are beginning to turn to LLMs to help viewers navigate overwhelming choice and fragmented catalogs. The details tested \u2014 from summaries and cast to genre, release years and runtimes \u2014 are the same ones audiences use to decide what to watch and services use to describe, organize and recommend content. The results show why AI-powered content discovery is only as good as the data behind the experience.<\/span><\/p>\n \u201cViewers don\u2019t care where a bad answer comes from. If it\u2019s wrong, they blame the service,\u201d said Tyler Bell, senior vice president of product at Gracenote. \u201cThat\u2019s why grounding matters. For companies building the next generation of entertainment discovery, generative AI will only deliver on its promise when it is grounded in verified content intelligence that replaces plausible guesses with accurate facts \u2014 reducing friction, deepening engagement and strengthening loyalty.\u201d\u00a0<\/span><\/p>\n Additional insights include:<\/span><\/p>\n Similar titles led the LLM to the wrong content.<\/span> In one example, the ungrounded model returned the correct title and year for the 2025 thriller \u201cHeel,\u201d but pulled the description, cast and genre from \u201cHeels,\u201d a Starz drama series that ran from 2021 to 2023. In another, it conflated the 2024 horror-thriller \u201cTrucker\u201d with a 2008 movie of the same name.<\/span><\/p>\n<\/li>\n Recent content exposed major blind spots.<\/span> The ungrounded model was unable to provide information about several new titles, including \u201cGOAT,\u201d a 2026 film that earned nearly $200 million globally before hitting Netflix.<\/span><\/p>\n<\/li>\n Even core cast information proved unreliable.<\/span> For the top 100 U.S. movies, only 53% of the ungrounded LLM\u2019s primary-actor responses matched the grounded data.<\/span><\/p>\n<\/li>\n<\/ul>\n As the report makes clear, no LLM is hallucination-free in 2026 \u2014 a particular risk for AI systems expected to deliver accurate, current entertainment answers at scale. For companies building AI-powered search, discovery and recommendation experiences, grounding helps turn model capability into viewer trust. Gracenote\u2019s authoritative content intelligence provides that foundation in two ways: via direct data licensing or its <\/span>Video MCP Server<\/span><\/a>, which connects to the company\u2019s global entertainment knowledge graph. With this access, LLMs can move beyond plausible-sounding hallucinations and deliver more reliable responses that reduce viewer friction, deepen engagement and strengthen loyalty.<\/span><\/p>\n Gracenote will share findings from the report at the StreamTV Show on June 18 in Denver, where Nandita Arora, senior director of product at Gracenote, joins the panel \u201c<\/span>Reimagining Content Discovery<\/span><\/a>.\u201d The session will explore how AI, personalization, unified search and new user experience approaches are reshaping how streaming services connect viewers with content.<\/span><\/p>\n The full report, \u201cPlot Holes in AI: Why Ungrounded LLMs Can\u2019t Fix Content Discovery,\u201d<\/span> <\/span>is available for download <\/span>here<\/span><\/a>.\u00a0<\/span><\/p>\n Gracenote tested 2,600 popular movie and TV titles across 13 countries: Australia, Brazil, Canada, France, Germany, Japan, Mexico, the Netherlands, South Korea, Spain, Sweden, the U.K. and the U.S. The study compared responses from an ungrounded LLM instructed to answer from training data alone, with responses grounded in Gracenote global video data via an MCP server. Responses were evaluated across objective attributes, including title, description, actors, genres, release year and runtime where applicable. Because these attributes can be independently verified, the results provide a quantified view of how grounding affects the accuracy and reliability of AI-generated entertainment responses.<\/span><\/p>\n<\/div>\n\n
Methodology<\/span><\/h2>\n