Why your LLM thinks “The Madison” is in Wisconsin

By Tyler Bell, SVP, Product

Here’s a familiar scenario: With so much TV content available, you find yourself asking friends and family for show recommendations to save yourself from endless scrolling. Thankfully, a friend who knows you’ve enjoyed Yellowstone, Landman and Mayor of Kingstown recommends Taylor Sheridan’s latest TV series, The Madison.

Eager to start the show, you ask your voice-enabled remote control to find and play the first episode. But instead of serving up the Michelle Pfeiffer-led drama about a family that moves from New York City to the wilds of Montana, you’re shown a reality TV show about a group of young adults living in Madison, Wisconsin.

This might sound unlikely, given the fanfare around Taylor Sheridan and his empire of popular TV shows. It is, however, the exact response that an ungrounded LLM provided when we asked it for information about the latest program in the Sheridan TV universe.

As surprising as the response might seem, it shouldn’t be. That’s because LLMs have some very basic limitations.

While LLMs excel at reasoning and inference, they aren’t built to articulate facts. As prediction engines, their sole function is next-token prediction. And in the example above, the LLM’s knowledge cutoff prevents it from knowing anything that happened after its training period ended, including the March 2026 premiere of The Madison. 

For an LLM, response accuracy is entirely dependent on optimized plausibility, not fact. Data and training can improve results, but no one should expect factual certainty from a machine trained to reply based on probability. As with popular chatbots like ChatGPT and Gemini, the LLM within any video distribution service needs grounding¹ to verify, correct and enhance the responses it provides.

Unlike those popular chatbots, however, LLMs used for content search and discovery need domain-specific grounding. Grounding with authoritative entertainment data, for example, would prevent an ungrounded LLM from returning information about a Starz TV series called Heels that aired from 2021 to 2023 when asked about a 2025 feature film with a very similar name: Heel.

As content catalogs grow, the challenge to find content is real, as TV viewers claim they now spend 14 minutes looking for something to watch². LLMs have the ability to solve content discovery challenges for audiences, but they can’t do it by themselves.

Grounding keeps LLMs in sync with the times

Backed only by their training data, LLMs aren’t comprehensive knowledge banks. This foundational characteristic sits at the core of why LLMs need external data to ensure that the answers they provide are current and accurate. Without external grounding, for example, knowledge cutoffs render LLMs blind to any content released after they’re deployed—a notable limitation when we consider how many new TV shows and movies get released each year.

While frustrating to audiences, the best possible response from an LLM when asked about a new release might be one that simply acknowledges the constraints of its training data. In a recent Gracenote study, an ungrounded LLM released in May 2025 did just that, noting that it had no information about a variety of recent mainstream films released in 2025 and 2026.

More commonly, however, LLMs fabricate what they don’t know (i.e., they “hallucinate”), and they do it confidently. This introduces significant user experience risks in the world of entertainment, especially amid growing fragmentation and the preponderance of rebooted titles with the same name as their predecessors.

To better understand the impact of hallucinations on content discovery, our recent study evaluated how much information an ungrounded LLM fabricated about the top 100 TV episodes and top 100 movies in 13 countries. Across the 2,600 titles, the ungrounded LLM fabricated 100% of the attribute-level information for 506 titles (nearly 20% of the total).

LLMs can’t deliver factual certainty

Combined, the infrastructure limitations associated with finite training data, next-token prediction and a lack of grounding data deliver an unfavorable experience for TV viewers. In the end, a positive user experience depends on viewers finding what they’re looking for. Here, ungrounded LLMs aren’t up to the task simply because factual accuracy is an impossibility.

For streaming services, ungrounded AI isn’t theoretical. If an assistant invents a storyline, provides the wrong cast or confuses similar titles, viewers aren’t going to blame the model. They’re going to blame the experience, making grounding essential to trust, retention and monetization.

That risk is what makes solving content discovery so urgent, and the biggest challenge audiences now face is finding something to watch. LLMs will play a critical role here, but successful AI strategies won’t be built with bigger models. 

Instead, winning media companies and streaming providers will build complete and current content intelligence directly in their AI stack, delivered through licensed datasets, MCP servers or other connections to an authoritative knowledge graph.

Agents and models: But the chatbot does this well…

Here’s a critically important distinction in the world of AI: when you, as a consumer, interact with ChatGPT, Gemini or Claude, you’re not experiencing the model that you would be professionally licensing from OpenAI, Google or Anthropic, respectively. Instead, you’re engaging with an agent, an implementation of the underlying model that is combined with a significant number of ancillary tools and data. 

Have a math question? The model federates that to an arithmetic tool. Want to know about a recent event, sports score or the latest news? The model passes that query off to a dedicated tool that searches the web. These consumer-facing chatbots rely on tens—if not hundreds—of domain- and task-specific tools and data sources to deliver the informed consumer experience that they provide.

When you employ an LLM in your entertainment stack, you don’t have access to the same tool and data as the consumer chatbot. The LLM is an incomplete solution. Here, you must source your own data and tooling to:

• Ground LLM responses to prevent endemic hallucinations
• Provide ancillary capabilities that the LLM cannot perform by itself, and 
• Ensure that the model has access to up-to-date information 

Specific tools, such as MCP servers, are combined with behavioral instructions (the system prompt) and business logic within an agent. You will likely implement a different agent for each AI-related task in your entertainment stack, but they will all use the same LLM, and much of the same tooling.

This article originally appeared on Streaming Media.