Will a Gem give more complete answers with a massive dump of unstructured info or with a lighter, structured load of text?

Optimizing Answer Completeness in AI Gems: Unstructured Data vs. Structured Summaries

As developers and enthusiasts delve deeper into creating effective AI-powered tools, a common challenge emerges: how to best prepare input data to facilitate comprehensive and accurate responses. This is especially relevant when building “Gems” — custom AI modules or plugins designed to answer specific queries. A key question is whether to feed these Gems a large volume of unstructured information or to process this content into a more organized, structured format.

Consider a scenario where the source material is a one-hour technical tutorial video on YouTube. The goal is to utilize this content to enable the Gem to provide detailed and complete answers. There are two primary approaches:

1. Directly Input the Raw Transcription:
   Feeding the Gem with the complete transcript of the video in a single, unaltered file. This method provides the AI with the raw, unfiltered data, capturing every nuance and detail present in the original content.

2. Use a Summarization and Organization Tool, such as NotebookLM:
   Alternatively, processing the transcript through a tool like NotebookLM (or similar AI summarizers) to generate a condensed, organized overview of the content. The summarized output aims to structure the key concepts, sections, and core ideas more clearly, potentially aiding the Gem in delivering targeted, coherent responses.

Which approach yields better results?

The answer largely depends on the specific objectives and constraints. Feeding raw, unstructured data offers the AI access to all available information, but it may make it challenging for the Gem to identify relevant details efficiently, especially if the dataset is large and complex. Conversely, a well-organized summary can help the Gem focus on the most pertinent topics, potentially improving response clarity and relevance.

In practice, many developers advocate for a hybrid strategy:
– Use summarization tools to distill large datasets into manageable, structured chunks.
– Complement these with raw transcripts or data dumps when completeness is paramount, and the AI can leverage context to navigate unstructured information effectively.

Conclusion
To build a Gem capable of providing the most thorough answers, consider experimenting with both strategies. Assess the quality and depth of responses generated from unstructured data versus structured summaries, and tailor your approach accordingly. The optimal solution may involve iterative refinement—balancing comprehensive data inclusion with effective organization—to meet your specific needs for detail and accuracy.

By thoughtfully selecting and preparing your input data, you can