In AI for Live Video, Context Is King, and Less Is More
In 2026 there seems to be no limit to what AI knows, what it thinks it knows, and how blindingly fast it can answer any question or complete any task you throw its way. (To say nothing of the staggering water footprint it takes to cool its data centers' servers.) And when AI systems from live transcription to disease detection claim to be 95-99% accurate, that sounds pretty impressive.
But like a disease with a 1% mortality rate, a 99% survival rate is pretty good on an individual level (“I’ll take those odds!”) but catastrophic across an entire population. Likewise, if an AI system hallucinates at a rate of 1% across all of the tasks we place before it, those failures quickly add up. As a result, the more you rely on AI, and the more routine tasks you routinely offload to it, the more it can feel like you’re flying blind, and the more imperative it is that the LLM or AI stack you’re relying on needs to understand not only the type of task you’re asking it to perform; it needs to understand you.
There are few phrases we hear more frequency around AI these days than “Context is King,” and with good reason. Shortly before press time I attended a webinar titled “Context Is King: Best Practices for AI in Live Video,” featuring two presenters immediately familiar to most Streaming Media Global readers—longtime columnist and Norsk CBDO & Co-Founder Dom Robinson, and CEO and Co-Founder Adrian Roe, a frequent speaker on our physical and virtual conference stages—as well as CaptionHub CEO & Founder Tom Bridges. Although we’re always hearing about AI-driven solutions that can streamline, simplify, or turbocharge elements of the livestreaming workflow, or relieve streaming pros of the need to juggle or manage so many of those tasks themselves, this webinar focused not so much on what AI-enabled tools can do for us as what operator-supplied context and context-aware design can do to optimize those tools and more reliably provide the value we want, and increasingly rely on them to provide.
AI and contextual minimalism
Focusing on context and AI in the context of Norsk’s no-code/low-code live streaming workflow builder Norsk Studio, Roe made one fascinating point that essentially flipped on its head my prior understanding of how to leverage AI in live streaming workflows, although it also makes a great deal of sense. Often we hear about the ways AI helps optimize live streams in terms of aspects like observability, and the ways AI-driven monitoring can process unimaginable amounts of data for real-time anomaly detection and the like.
But as Roe pointed out, when it comes to educating your LLM about your intent to improve the reliability of its live decision-making, throwing more information at it just because you can is often counter-productive. Roe even went so far as to revise our new mantra by saying “minimal context is king. I want the LLM to know as little as possible to do the job. Just as is the case with human beings, we want the AI to find what it needs and little more.”
AI wants to be your sledgehammer
Bridges made a similar point about educating your AI before relying on it to improve transcription and captioning and deliver better viewer experiences in a live environment.
Acknowledging that it’s “really hard to put AI in the middle of captioning,” he explained that CaptionHub has three interlocking goals when it comes to captioning: accuracy, proper text segmentation, and—largely as a result of succeeding with the first two—limiting the viewer attention required to absorb the captions at 20–25%. The most effective means of achieving these goals, of course, is giving the AI more context via custom dictionaries, termbases, and the like. Again, it’s the human intervention that elevates the AI, not the AI itself. “There’s a tendency to say, ‘We’ve got the sledgehammer that is AI’ and look at everything as if it is a nut.”
In or out of a live streaming context, not every nut is AI’s to crack.