ChatGPT struggles to parse my 80 video transcript dataset

Challenges in Utilizing ChatGPT to Analyze Extensive Video Transcript Datasets

In the realm of content management and multimedia processing, leveraging advanced AI tools like ChatGPT can streamline workflows significantly. However, users often encounter limitations when working with large, structured datasets—particularly when attempting to extract meaningful insights or generate creative outputs. Here’s an exploration of such a challenge based on a real-world scenario.

The Dataset and Objective

A content creator compiled an Excel spreadsheet containing transcripts from 80 videos. The dataset is organized with ID numbers in Column A and corresponding transcript segments in Column B. The goal was to enable ChatGPT to analyze this dataset, understand the segments, and generate new video combinations by recombining selected clips.

For example, the intention was to produce new video segments such as:

New Video:
ID_003 (In: 00:05:31, Out: 00:08:12) +
ID_042 (In: 00:05:31, Out: 00:08:12) + …

The idea was to mix and match segments based on their timestamps and content for creative or editing purposes.

The Challenges Faced

Despite clear intentions and a well-structured dataset, several issues arose:

1. Confused Outputs:
   ChatGPT often produced outputs that included unrelated segments or misplaced timestamps, making the generated clips nonsensical or inconsistent.

2. Mid-sentence Timecodes:
   In some cases, the model inserted start or end timecodes amid incomplete sentences, resulting in fragmented content that wouldn’t translate well into actual video clips.

3. Data Format Limitations:
   The user attempted multiple formats—initially providing the data in Excel, then converting it into JSON—to facilitate better parsing. Still, the problem persisted.

4. Access Constraints in Custom GPT Models:
   When trying to upload the dataset into a custom GPT environment’s knowledge base, it failed to access or interpret certain transcript parts, suggesting limitations in how the AI processes large textual datasets or embedded data.

Potential Reasons for These Challenges

These issues are common when attempting to use language models for large-scale data parsing:

• Token Limitations:
  ChatGPT has a maximum token capacity for each input, which can be quickly exceeded with lengthy transcripts, leading to incomplete understanding.

• Lack of Structured Data Handling:
  While JSON or structured prompts can help, the models are not inherently designed as databases; they work