Model B, because a higher R-squared means it explains more variance in the data

Model A, because a lower MAE indicates better prediction accuracy on average

Model B, because higher R-squared always indicates a better model

Neither, because both models have significant trade-offs and need further tuning

High computational requirements can increase costs, but it also provides an opportunity to invest in more energy-efficient hardware.

Data privacy concerns limit the availability of training data, but this also drives the development of synthetic data generation.

Model interpretability is difficult, but it can also lead to advancements in explainable AI.

Bias in the training data can lead to unfair or harmful outputs, but it also presents an opportunity to develop more ethical AI.

Model A, because it has a lower R²

Model B, because it has a higher R²

Model A, because a lower MSE always indicates a better model

Model B, because it has a higher MSE

Use a different pre-trained language model without fine-tuning

Normalize the text by removing all technical terms

Fine-tune the embedding model specifically on the troubleshooting guides

Increase the size of the training dataset

Develop a custom LLM from scratch with domain-specific capabilities.

Implement a fine-tuning approach by training the LLM on a large dataset of company documents, then deploy the chatbot.

Use prompt engineering to customize responses, and continuously refine the prompts based on real-world feedback to improve the LLM’s performance.

Pre-process and structure all possible customer queries, mapping them to specific responses before training the model.

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