Hallucinations: What’s the risk?
As artificial intelligence (AI) adoption and usage grow, so does awareness of its potential to provide incorrect statements as fact. These “hallucinations,” plausible-sounding but false information, are a known risk associated with using AI, but many aren’t aware of how severe they can be. When people don’t understand the risks associated with hallucinations, they aren’t able to assess their implications.
In the media industry, large language models (LLMs), a type of generative AI trained to understand and generate human language, will become the default engines that deliver next-gen entertainment experiences. Success on this front, however, hinges on backstopping LLMs with credible, external data sources to ensure the delivery of accurate, current and relevant results. This process is called “grounding.”
Why LLMs hallucinate
Importantly, LLMs are not databases, and they do not store data in the traditional sense. They are probability matrices trained on exhaustive, but finite, data. As a result, they synthesize responses rather than retrieving and articulating facts. In practice, LLMs’ primary job is to predict the most likely piece of text (e.g., a token) in a statistically mandated pattern. If the most linguistically plausible next word in a sequence happens to be incorrect, the LLM will deliver it anyway because it fits the pattern.
So, the essential, probabilistic nature of the technology itself is the primary source of hallucinations, but this technological vulnerability is compounded by the data the models are trained on. Models are especially prone to hallucination when they are prompted to respond to answer questions when there is little or no topical data in their training dataset or the relevant training data is conflicting. This is particularly evident in media use cases where questions are asked of recent releases, recent events (such as the latest Academy Awards) and lesser-known or fringe titles.
The internet bears quite a bit of the blame here, as it serves as a primary dataset for LLM training. Grounding an LLM with real-world, verified data is the primary defense against hallucinations. Grounding methods vary, as do the data sources they tap into. As a result, any individual LLM is only as reliable as the data it can access. As of 2026, no LLMs are hallucination free, and given the nature of the technology, this reality is unlikely to change anytime soon. Grounding, really, is the only viable approach to mitigating hallucinations.
LLMs in entertainment
In step with broader AI adoption and use, entertainment providers are looking to level up the content experiences they offer their customers. Here, AI offers significant advantages over traditional database and search technologies. Powerful ranking and sorting capabilities, hyper-personalized recommendations, harmonization of content catalogs and conversational search are among the key advantages that LLMs can provide.
Metadata underpins the success of any LLM tasked with revolutionizing the way people experience content. While the consumer may only see 10 or 20 metadata attributes for a particular movie or TV show, streaming services and studios often track hundreds—even thousands—of data points for individual titles.
Importantly, the degree of hallucination risk is not consistent across all metadata attributes. Certain attributes, such as content type and genre, pose a very low risk of hallucinations because LLMs excel when probability responses are focused on structured logic and categorical mapping.
When metadata attributes are highly unique, however, the risk of hallucination increases significantly. Content IDs and mathematical attributes, for example, carry very high hallucination risk. In these cases, LLMs will confidently “guess” a number that it believes is plausible, but is factually wrong. For example, numbers are often broken into sub-tokens. So, an LLM might see the number 154 as 15 and 4. When constructing these, the “math” often breaks, leading to “off-by-one” errors.
Season and episode numbers are particularly challenging because of how LLMs function. For example, if an LLM has seen 1,000 episodes of the Simpsons, it knows there is a season 10, episode 5. If a viewer asks about a niche show with only six episodes, it might still lean toward a higher number because most of the shows it was trained on have longer seasons.
Assessing hallucination risk by metadata attribute
Given the wide range of metadata attributes that exist, not all are universally susceptible to hallucinations.
The risk of hallucination about a director, for example, is different for large studio productions than small, independent movies. Here, credit confusion could lead an LLM to hallucinate a producer or famous contemporary film maker as the director.
Let’s dig into the hallucination risk across specific content types and metadata attributes.
General attributes
| Attribute | Hallucination risk | Reasoning |
| Gracenote TMSID (or any identifier) | Critical | Non-semantic strings: IDs are semantic nonsense to a language model, so LLMs will simply invent a string that looks like identifiers it has seen previously. LLMs will not report the correct TMSID for any title seen outside occasional identifiers seen in Gracenote’s public documentation. |
| Type | Very low | Structural logic: Models usually know if they’re talking about a movie or a show based on context. It’s rare for them to hallucinate a “movie” as an “episode” if the title is provided. However, models will be prone to confusing shows and movies with the same title, especially if they share a cast member. |
| Actors | Low | Association bias: LLMs have high accuracy for leading names, but they may hallucinate an actor into a project they were never in, simply because they frequently work with that director or within a related genre. |
| Genre | Low | Categorical mapping: There is, in principle, a finite list of genres. LLMs are generally good at classifying “The Batman” as “action/crime,” though they may miss sub-genres, and their responses will not match a standard taxonomy. |
| Description | Low | Generative strength: LLMs can generally synthesize a plausible summary. This is “soft” data, where “accuracy” is subjective. This assumes, however, that LLMs are not confusing or blending titles of the same name. The description will not comply with editorial standards (e.g. no spoilers) unless rules are specifically requested. |
| Images | Critical | No rights clearance: LLMs cannot verify if an image URL is live or relevant. They will often hallucinate a likely path, and any images that do resolve correctly will be untyped, with unknown usage rights. |
| Duration | Medium | Regress to the mean: LLMs tend to guess standard lengths (22m, 44m, 90m, 120m) rather than the specific, frame-accurate runtime. |
Movie attributes
| Attribute | Hallucination risk | Reasoning |
| Year | Medium | Historical marker: Release years for movies are “anchor facts” in LLM training data. Risk increases for obscure indie films and unreleased projects. However, Gracenote research has shown that release years are not infrequently hallucinated off-by-one. |
| Director | Medium | Credit confusion: LLMs are less prone to hallucinate directors for famous films. For smaller films, LLMs may hallucinate the producer or a more famous contemporary, assigning them the director role. |
TV show attributes
| Attribute | Hallucination risk | Reasoning |
| Year range | Medium | Drift: LLMs commonly report the start year correctly, but will hallucinate an end year if the show was cancelled or renewed after the model’s training cutoff, if the show continues. |
| Creator | Medium | Role confusion: LLMs often struggle with specific roles in a production. It might know “Vince Gilligan created Breaking Bad,” but they commonly hallucinate the relationship between people and their involvement with a specific title. |
| Season count | High | Knowledge cutoff: A show that has five seasons today might have only had three when the model was trained. Therefore, the LLM will state the old number as “fact.” Generally, LLMs are not reliable for any integer, as numbers are not ‘stored’ as fact. Rather, they are predicted based on similar data. |
TV episode and season attributes
| Attribute | Hallucination risk | Reasoning |
| Episode title | High | Semantic guessing: For famous episodes (e.g., “The Rains of Castamere”), accuracy is high. For generic episodes, LLMs will hallucinate a title that “sounds like” it belongs to that show (e.g., hallucinating a Friends episode called “The One with the Coffee”). |
| Season number | High | Predictive probability: LLMs treat season numbers as “likely sequences.” If a show is long running, it may guess season 4 instead of season 5 because both are equally “likely” in its weights. |
| Episode number | High | Lack of indexing: Without grounding, the LLM is just guessing the position of an episode. It often suffers from “off-by-one” errors. |
| Original air date | High | Pattern matching: LLMs may know a show aired on “Thursdays in 2014” and hallucinate a plausible Thursday date that is factually incorrect. |
| Director | High | Credit dilution: Episodic directors change constantly. Unless an episode has a famous “guest director” (e.g., Tarantino directing CSI), LLMs will typically guess the showrunner or a frequent series director. |
The mathematical path of least resistance
LLMs are trained to minimize “loss,” meaning they want to be as “correct” as possible, according to their training data. In a massive dataset, certain patterns appear more often than others.
With regard to release years: In the training data, the string “Star Wars” is followed by “1977” millions of times. The probability of “1977” following “Star Wars” is nearly 100%.
For seasons and episodes, “season 1” for a mid-tier show appears in the training data much more often than “season 7.” If the LLM is unsure of the facts, it will default to the most frequent pattern in its training data, which commonly contains lower numbers (1, 2, or 3).
Semantic gravity
“Likely sequences” are also driven by the style of the content. This is why episode titles are so susceptible to hallucination.If you ask an LLM to name an episode of Friends, it knows the pattern: “The One With…”
- The reality: There is no episode called “The One With the Solar Eclipse.”
- The hallucination: Because “The One With…” is a high-probability prefix, and “Solar Eclipse” is a common TV trope (it tells the audience “things are about to get weird”), the LLM combines them into a “likely sequence.” The answer sounds 100% authentic because it follows the semantic rules of Friends episode titles, even if the answer is factually incorrect.
The integer problem: tokens vs. numbers
LLMs don’t “count” the way humans do. They see numbers as fragments, so the number 154 might be processed as two tokens: 15 and 4.
When an ungrounded LLM predicts an episode number, it isn’t looking at a database. It’s asking: “In a sequence of numbers following this show’s title, what digit usually comes next?”
If the training data shows the show has roughly 20 episodes per season, and the LLM has already generated “season 2,” it will statistically favor any number between 1 and 20. The specific choice of “12” vs “13” is often a coin toss based on “noise” in the model, and you could get different answers to the same prompt.
Why hallucinations look so confident
An LLM doesn’t have a “I don’t know” state unless it’s specifically tuned for it. Most commonly, it enters a “likely sequence” and generates tokens with high mathematical confidence, a “probability map.” Here’s an example Probability Map with regards to director names:
Input: The director of the movie Titanic (1997) is…
Next token probabilities:
- James: 99.2%
- Steven: 0.3%
- George: 0.1%
The expected outcome due to the overwhelming written association of James Cameron and the film Titanic.
Input: The director of the TV episode ‘The Fly’ is…
Next token probabilities:
- Vince (Showrunner, Breaking Bad): 45%
- Rian (Actual Director, Breaking Bad, S03 E10): 30%
- Michelle (Frequent Director): 20%
In this second example, the LLM will pick Vince (Gilligan) because he is more “likely” to be associated with the show’s text overall, even though he didn’t direct that specific episode. Because there is less written material relating to this episode (compared with the Titanic example) the relatively insignificant training data means that the probability map is more likely to produce an incorrect answer.
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