Making search more accurate for LLMs with large data sets

[Tetsujin13]

Hi dave, I think you mentioned during one of your videos about an issue with AIs and large training data sets. The AI would have trouble searching for the correct information.
I came across an article about discovering a new hybrid brain cell that was a hybrid between an astrocyte and neuron, that shared functions with both and was mostly positioned at junctions between the two brain cells.
What caught my attention was that this hybrid cell was able to modulate the neural pathway based on activating and deactivating a compound at comparable speed as neural transmittance.

I immediately thought about how this could translate into AI, and lazy me asked GPT. So here's what it came up with to increase search performance:

"To create a more accurate and efficient search system for large training datasets, consider developing a solution that integrates a graph-based data indexing approach with a dynamic, context-aware search algorithm. Here's a summary of the proposed solution:

1. Graph-Based Data Structure: Utilize a directed acyclic graph (DAG) to index the data. Each node in this graph represents a data point or a cluster of related data, while edges denote the relationships or connections between these points. This structure allows for a flexible representation of data relationships and hierarchies.

2. Contextual Analysis: Implement a contextual analysis component that interprets the search query, possibly using natural language processing (NLP) techniques. This analysis is crucial for understanding the search intent and the relevant context of the query.

3. Dynamic Path Activation: Design an algorithm that selects and activates certain paths within the graph based on the context derived from the search query. This algorithm determines which nodes and their connections are most pertinent to the current search, optimizing the search path for relevance and efficiency.

4. Adaptive Search Algorithm: The search algorithm should adaptively traverse the graph, focusing primarily on the activated, relevant paths. It dynamically adjusts its traversal strategy based on the activation status of the nodes, efficiently navigating through the most pertinent data.

5. Feedback Loop for Continuous Learning: Integrate a feedback mechanism that learns from each search iteration. This feedback is used to refine the path activation algorithm, enhancing the accuracy and speed of future searches.

6. Optimization for Performance: Apply techniques such as caching frequently accessed data and optimizing the graph structure for quicker traversal to ensure the system remains performant, even as the dataset grows.

7. Scalable and Modular Design: Ensure the system is scalable to accommodate growing data and adaptable to different types of data and search requirements. A modular design allows for easy integration of new data or updating existing structures.

8. Parallel Processing: Where feasible, implement parallel processing to explore multiple graph paths simultaneously, further enhancing search speed.

By combining these elements, the proposed solution aims to significantly improve search accuracy and efficiency in large training datasets, leveraging the nuanced relationships between data points and adapting dynamically to the context of each query."