Utilizing token classification to label individual words in the emails related to each category.

Applying a few-shot learning approach using labeled examples for each issue type within the prompt.

Building a custom model from scratch specifically trained on your company’s emails.

Fine-tuning the entire LLM on the labeled email data for each category.

Enable batch processing for inference to handle multiple requests simultaneously

Migrate the application to a server with higher CPU capacity

Implement model quantization of the LLM

Increase the number of layers in the model to enhance its decision-making capabilities

Retrain the LLM exclusively on the company's previous projects without further adjustments.

Focus on improving the LLM's general code quality without enforcing specific coding standards.

Allow developers to manually review the code for adherence to standards after generation.

Integrate a code linting tool that automatically checks the LLM's output for compliance with coding standards.

Model B, because it has a higher loss, indicating it has learned more generalized features

Model A, because lower Cross-Entropy Loss indicates better model confidence

Model B, because a higher F1-score indicates better balance between precision and recall

Neither, because the difference in Cross-Entropy Loss is more significant than the difference in F1-score

TensorFlow for training, NumPy for embeddings, and a NoSQL database

spaCy for parsing, Keras for embeddings, and a traditional relational database

spaCy for text processing, Hugging Face transformers for embeddings, and a vector database

NumPy for text embeddings, pandas for data storage, and Matplotlib for visualization

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