Ungrounded LLM Fabricates Every Detail for Nearly 1 in 5 Movie and TV Titles Tested, New Gracenote Report Finds

Study underscores the need for authoritative content intelligence to build trusted search, discovery and recommendation experiences powered by generative AI

NEW YORK, June 10, 2026 /PRNewswire/ -- Gracenote, the content intelligence business unit of Nielsen, today released its latest report, "Plot holes in AI: Why Ungrounded LLMs Can't Fix Content Discovery." 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.

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 — from summaries and cast to genre, release years and runtimes — 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.

"Viewers don't care where a bad answer comes from. If it's wrong, they blame the service," said Tyler Bell, senior vice president of product at Gracenote. "That's 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 — reducing friction, deepening engagement and strengthening loyalty."

Additional insights include:

• Similar titles led the LLM to the wrong content. In one example, the ungrounded model returned the correct title and year for the 2025 thriller "Heel," but pulled the description, cast and genre from "Heels," a Starz drama series that ran from 2021 to 2023. In another, it conflated the 2024 horror-thriller "Trucker" with a 2008 movie of the same name.
• Recent content exposed major blind spots. The ungrounded model was unable to provide information about several new titles, including "GOAT," a 2026 film that earned nearly $200 million globally before hitting Netflix.
• Even core cast information proved unreliable. For the top 100 U.S. movies, only 53% of the ungrounded LLM's primary-actor responses matched the grounded data.

As the report makes clear, no LLM is hallucination-free in 2026 — 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's authoritative content intelligence provides that foundation in two ways: via direct data licensing or its Video MCP Server, which connects to the company's 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.

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 "Reimagining Content Discovery." The session will explore how AI, personalization, unified search and new user experience approaches are reshaping how streaming services connect viewers with content.

The full report, "Plot Holes in AI: Why Ungrounded LLMs Can't Fix Content Discovery," is available for download here.

Methodology

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.

About Gracenote

Gracenote is the content intelligence business unit of Nielsen. We standardize the way the global media and entertainment ecosystem indexes content and associated metadata, allowing it to flow between creators, distributors, platforms and advertisers. By providing unmatched depth across 50M+ titles and 80K+ channels and catalogs, we power the modern search, discovery and navigation experiences that connect people to the TV, movies, music and sports they love—in 70+ languages across 80+ countries. For more information, visit Gracenote.com or follow us on LinkedIn.

SOURCE Gracenote