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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