Knowledge Engineering to Increase Nuanced Understanding of Unstructured Data

Utilise knowledge engineering such as visual representations of the knowledge (can use text based or mermaid) in prompt loops to enable AI systems to better understand and process complex information. This approach helps in structuring (unstructured) data in a way that is more accessible and interpretable for AI, facilitating improved decision-making and problem-solving capabilities. By incorporating visual elements into the knowledge representation, AI systems can more effectively identify patterns, relationships, and insights, ultimately enhancing their performance and accuracy in various tasks--essentially turning unstructured into structure. Of course this requires prompt engineering but it isn't too complex. Think knowledge reps suchs as personalised/granular knowledge graphs/interconnection mapping/semantic networks/detailed flowcharts that map out the intricacies of the data. The key lies in combining advanced knowledge representation methods with sophisticated visualisation techniques and personalized knowledge graphs. Could even combine with Domain-Specific Knowledge Representations for increased precision in responding in reasoning.

• Wils