FullFrame: Applying AI to Film/Television Development

FullFrame shares insights about their leading script and book coverage solution.

Want your weekends back? Reimagine your development workflow with the leading AI script and book coverage.

📝 Streamline Your Coverage Workflow and say goodbye to hours of tedious summarizing. Within minutes, FullFrame generates comprehensive, automated coverage and logs your material.

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FullFrame includes a ~ 2-3 page summary that strikes the right balance between readability and detail. Plus, you’ll get a character breakdown and feature comps.

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Our users span notable writers, directors, studio execs, producers, and reps. Start your trial today and join the Hollywood professionals who are elevating their creative process with FullFrame, leaving you more time to do what you love: create. 

Go check it out! 👉FullFrame website

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FullFrame: Utilizing LLMs for Film/Television Development

So what sets FullFrame apart? Here are a few discoveries we’ve made while building applications for entertainment with large language models (LLMs), and specifically coverage of longer form content like scripts and books:

Output Length

We’ve found the most reliable predictor of synopsis quality is length, which is difficult to consistently achieve using a base LLM. While preferences vary, on average our users expect a full synopsis to be 2-3 pages and we’ve gone to great lengths to hit that mark, which tends to strike the balance of readability and detail.

Evaluations

A critical part of LLM-assisted coverage is ensuring coverage quality - not just evaluating the LLM generated content, but evaluating the evaluations. We’ve created a proprietary set of tests to judge whether you are being served the best possible output we could generate. In simplest terms, some examples of this might be “Did the output cover the entirety of the script or book?,” and then, “Was our check for completion correct?”

Context Utilization

While today’s models can technically ingest an entire script or entire book, we’ve found there’s significant variation in how models perform at retrieving and summarizing all of the content provided to them. Many of our users have tried this for themselves and found the task is more daunting than it seems, particularly with longer form content like 100+ page scripts and 300+ page books. In FullFrame, we’ve built a pipeline for long form content that consistently extracts detail from start to finish and reduces the “lost in the middle” effect that users often complain about with base LLMs.

If you are interested in using LLMs in entertainment, we’d love to talk! Reach out to us at: https://fullfr.me/contact_us.