Part 12/14:
While embeddings power many innovative systems, challenges remain:
Hallucinations: When LLMs generate inaccurate content, often due to training data biases or insufficient domain fine-tuning.
Optimal Embedding Size: Smaller embeddings are faster but may lack detail; larger embeddings are more precise but computationally intensive. Domain size and data complexity influence this balance.
Indexing Strategies: Developing custom indexers and wrappers can improve search performance, especially for specific enterprise needs.
Data Privacy & Security: Embedding sensitive data requires robust encryption and access control mechanisms.