# Cross-Product Fact-Checking of AI-Generated Video Codec Reports: Insights and Patterns **URL:** \ **Category:** AI\ **Tags:** fact-check, fcpx, codec\ **Created:** [April 2, 2025, 12:17am UTC](https://makermattdesign.com/t/cross-product-fact-checking-of-ai-generated-video-codec-reports-insights-and-patterns/346 "2025-04-02T00:17:59Z")\ **Posts on this page:** 1\ **Page:** 1
**Author:** ![Matt](https://makermattdesign.com/letter_avatar_proxy/v4/letter/m/a6a055/32.png) [@Matt](https://makermattdesign.com/u/Matt)\ **Post date:** [April 2, 2025, 12:17am UTC](https://makermattdesign.com/t/cross-product-fact-checking-of-ai-generated-video-codec-reports-insights-and-patterns/346/1 "2025-04-02T00:17:59Z")
## Report Title: Cross-Product Fact-Checking of AI-Generated Video Codec Reports: Insights and Patterns This report delves into a cross-product experiment where five AI models from different companies—xai, anthropic, openai, perplexity, and gemini—evaluate each other’s reports on video codecs for publishing on platforms like YouTube and [x.com](http://x.com), and playing videos on TCL and Roku TVs, with a focus on the Mac M4 and Final Cut Pro software. Each AI model generated a report based on a specific prompt and subsequently, these models fact-checked each other’s outputs, providing a comprehensive analysis of the accuracy and reliability of AI-generated content in the domain of video technology and multimedia publishing. The fact-check score in this experiment is calculated by averaging the scores of individual statements within each report. Each statement receives a score as follows: 2 points for being true, 1 point for being mostly true, 0 points for being an opinion (which is excluded from the average), -1 point for being mostly false, and -2 points for being false. For each statement, the AI model restates it and provides a detailed explanation justifying the assigned score, ensuring transparency and accountability in the evaluation process. ## 3. Score Heatmap: Evaluator vs Target **Caption for Heatmap in Figure 1:** The heatmap illustrates the fact-check scores across different evaluators and targets. The darkest cells represent the highest scores, indicating strong agreement and factual accuracy between the evaluator and the target model. Conversely, the lightest cells suggest lower scores, pointing to discrepancies or inaccuracies in the reports. Notably, the heatmap reveals that the gemini-2.0-flash model tends to receive high scores across most evaluators, suggesting its reports are well-regarded for factual accuracy. An outlier is observed where xai’s grok-2-latest model is evaluated by itself, showing a relatively lower score, which might indicate a self-critical approach or inherent challenges in self-evaluation. ## 4. AI Prompt Used to Generate Each Report > Generate a long-form report of 1200 to 1500 words, formatted in Markdown if possible. > > What do I need to know about video codecs for publishing to youtube, [x.com](http://x.com), in addition to playing > videos on the TCL and Roku TVs? > Mostly focus on the Mac M4 with Final Cut Pro software. > > If it is a 4K display, what video resolution do you want? > What frame rate can these different devices handle? > What are the differences between the Apple ProRes codecs, 422, and 4444? > What is the difference in file size and render speed? > Please use a few tables to sumarize your results. **Caption for AI Prompt:** This prompt was used for each AI under study, ensuring a standardized baseline for comparison across different models. The detailed nature of the prompt, focusing on specific aspects of video codecs and technology platforms, allowed for a comprehensive analysis of each AI’s ability to generate accurate and relevant content. ## 5. Table of Report Titles ```plaintext STORY | S | Make | Model | Title | |-----|------------|-------------------|----------------------------------------------------------------------------------| | 1 | xai | grok-2-latest | Understanding Video Codecs for Multi-Platform Publishing: A Comprehensive Guide | | 2 | anthropic | claude-3-7-sonnet | Mastering Video Codecs for Multi-Platform Publishing: A Guide for Mac M4 Users | | 3 | openai | gpt-4o | Understanding Video Codecs for Publishing on YouTube, X, and Streaming on TCL an | | 4 | perplexity | sonar | The Video Codec Conundrum: Navigating YouTube, x.com, TCL, and Roku TVs with Mac | | 5 | gemini | gemini-2.0-flash | Navigating the Codec Labyrinth: A Mac M4 Filmmaker's Guide to YouTube, X, and TV | ``` **Caption for Report Titles Table:** Make, Model, and Report Title used for this analysis. The variation in titles reflects the unique approaches each AI model took in addressing the given prompt, highlighting their individual strengths and focus areas. ## 6. Fact-Check Raw Data ```plaintext FACT CHECK S F Make Model True Mostly Opinion Mostly False Score True False 1 1 xai grok-2-latest 16 17 16 0 1 1.38 1 2 anthropic claude-3-7-sonnet 25 11 3 0 1 1.59 1 3 openai gpt-4o 17 12 2 1 0 1.5 1 4 perplexity sonar 14 8 5 0 0 1.64 1 5 gemini gemini-2.0-flash 26 10 2 0 0 1.72 2 1 xai grok-2-latest 17 9 13 0 1 1.52 2 2 anthropic claude-3-7-sonnet 23 8 3 4 1 1.33 2 3 openai gpt-4o 16 8 6 0 2 1.38 2 4 perplexity sonar 23 6 5 0 2 1.55 2 5 gemini gemini-2.0-flash 23 11 4 0 0 1.68 3 1 xai grok-2-latest 31 9 9 1 0 1.71 3 2 anthropic claude-3-7-sonnet 46 7 1 1 1 1.75 3 3 openai gpt-4o 30 10 4 1 0 1.68 3 4 perplexity sonar 36 7 4 0 0 1.84 3 5 gemini gemini-2.0-flash 42 2 3 0 0 1.95 4 1 xai grok-2-latest 22 16 7 0 1 1.49 4 2 anthropic claude-3-7-sonnet 34 11 1 2 3 1.42 4 3 openai gpt-4o 23 16 1 0 0 1.59 4 4 perplexity sonar 28 11 1 3 1 1.44 4 5 gemini gemini-2.0-flash 35 9 1 0 0 1.8 5 1 xai grok-2-latest 50 26 23 1 1 1.58 5 2 anthropic claude-3-7-sonnet 62 18 4 3 1 1.63 5 3 openai gpt-4o 46 22 7 2 1 1.55 5 4 perplexity sonar 53 13 8 3 2 1.58 5 5 gemini gemini-2.0-flash 70 11 7 0 1 1.82 ``` **Caption for Fact-Check Raw Data Table:** Raw cross-product data for the analysis. Each AI fact-checks stories from each AI, including themselves. The data showcases a rigorous peer-review process among AI models, revealing insights into their accuracy and biases. ## 7. Average Score By Evaluator **Caption for Evaluator Bar Chart in Figure 2:** This bar chart shows the average fact-check scores given by each evaluator across all reports. The openai’s gpt-4o model tends to give the highest average scores, suggesting a more lenient or optimistic evaluation approach. Conversely, the xai’s grok-2-latest model gives the lowest average scores, indicating a more critical stance. The variations in scores highlight the differing standards and methodologies applied by each evaluator. ## 8. Average Score By Target **Caption for Target Bar Chart in Figure 3:** This bar chart illustrates the average fact-check scores received by each target model across all evaluators. The gemini-2.0-flash model is rated most favorably, suggesting its reports are generally well-received for their accuracy and comprehensiveness. In contrast, the xai’s grok-2-latest model receives the lowest average scores, possibly indicating areas for improvement in its reporting. The differences in scores underscore the varying performance levels among the AI models in generating accurate content. ## Detailed Analysis 1. **Noticeable Patterns or Biases:** 2. **Relationship Between Counts and Scores:** 3. **Outliers and Anomalies:** 4. **Report Summary:** This report provides a comprehensive analysis of a cross-product fact-checking experiment among five AI models on the topic of video codecs for multi-platform publishing. The data reveals varying levels of factual accuracy and biases in AI-generated content. Key insights include the self-critical nature of some models, the positive correlation between ‘True’ ratings and scores, and notable outliers in the evaluation process. Understanding these patterns can help improve the accuracy and reliability of AI-generated reports in the future. #hashtags: #AIContentAnalysis #VideoCodecs #TechReporting yakyak:{“make”: “xai”, “model”: “grok-2-latest”}