Customisation Techniques

In order to make AI chatbots accessible and cost-effective for use by small and medium businesses, we make use of relatively simple prompt engineering behind the scenes. We use information provided by a user and turn that into a set of written instructions explaining to the chatbot what its role is. Prompt engineering is a simple and effective way to customize a chatbot based on a foundational large language model (LLM), but there are more advanced customisation techniques that we can assist you with as well.

Here is a list of customisation techniques, starting to prompt engineering and leading into more advanced techniques, that we can assist you with:

1. Prompt Engineering: Prompt engineering involves creatively designing prompts to guide LLMs towards generating the desired outputs. It includes crafting specific questions or statements and using few-shot learning by incorporating examples directly into the prompts to showcase the format and type of response desired. This technique is crucial for applications like customer service chatbots, where prompts steer conversations towards resolving queries, or educational tools that generate practice problems, guiding the model on how to format questions appropriately.

2. Hyperparameter Adjustments: Adjusting hyperparameters such as temperature, top-p, top-k, and maximum token length allows control over the creativity, diversity, and length of the model's outputs. This method is relatively straightforward and can significantly alter the output's quality and suitability for specific tasks. It finds use in creative writing apps, where users can tweak these parameters to generate stories with varying creativity levels and lengths.

3. Fine-tuning: Fine-tuning involves additional training cycles on specific datasets to enhance the model's performance in particular domains or tasks. Domain-specific fine-tuning improves the model's expertise in areas like legal texts or medical records, while task-specific fine-tuning optimizes the model for activities such as summarization or translation. This approach is applied in legal research assistants, summarization tools for scientific articles, and other specialized applications.

4. Plug-ins and Extensions: This customization level enhances LLM functionality by integrating external APIs, databases, or developing custom modules for specific preprocessing or postprocessing needs. For instance, market analysis tools might integrate with financial databases for real-time stock price information, or content moderation systems could include modules to filter out inappropriate content. This technique expands the model's capabilities beyond its initial training.

5. Reinforcement Learning from Human Feedback (RLHF): RLHF involves a feedback loop where human evaluations of the model's outputs lead to adjustments in its behaviour. This method is used to refine the model's responses to align more closely with human preferences or ethical guidelines, such as improving sentiment analysis accuracy in customer feedback tools through ongoing human reviewer feedback.

6. Pre-training Customization: Pre-training customization is the most effort-intensive technique, involving selecting specific datasets for pre-training to influence the model's knowledge base and biases or modifying the model's architecture to suit computational resources and application needs. It's employed in creating models with specialized knowledge, like a news summarization service trained on global news sources, or developing compact models for mobile devices for applications like offline language translation.

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