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Companies can utilize their own data to create generative chat-based AI models that can significantly improve productivity. Here’s a step-by-step process to leverage data for creating such AI models:

• Data Collection: Gather relevant data from various sources within the company, such as customer support logs, chat transcripts, email conversations, knowledge bases, and internal documentation. This data should represent real-life interactions and cover a broad range of topics.

• Data Preprocessing: Clean and preprocess the collected data to remove noise, duplicates, and irrelevant information. This step involves removing personally identifiable information (PII) and ensuring data privacy and compliance with regulations.

• Annotation and Labeling: Annotate the collected data to provide context and meaning to the AI model. For example, identify user intents, categorize responses, and label different aspects of the conversations. This step helps the model understand the structure and semantics of the data.

• Model Training: Utilize machine learning techniques, such as deep learning, to train a generative chat-based AI model, such as ChatGPT, on the annotated data.

• Iterative Training and Validation: Continuously refine and improve the model by iteratively training and validating it against new data. This process helps the model learn from feedback and adapt to specific user needs and preferences over time.

• Integration and Testing: Integrate the trained chat-based AI model into the company’s existing systems or communication channels. Conduct thorough testing to ensure the model’s accuracy, reliability, and responsiveness.

• Continuous Monitoring and Maintenance: Monitor the performance of the chat-based AI model in real-world scenarios and make necessary adjustments as new data becomes available. Regular updates and maintenance are crucial to ensure the model stays up-to-date and aligned with evolving business needs.

• User Feedback and Improvement: Encourage users to provide feedback on their interactions with the chat-based AI. Analyze user feedback to identify areas for improvement, address limitations, and refine the model to deliver a more satisfying user experience.

By leveraging their own data and following the steps outlined in this post, companies can develop custom generative chat-based AI models that understand specific domains, customer preferences, and internal workflows. These models can improve productivity by automating routine tasks, providing quick and accurate responses, and assisting employees in finding relevant information efficiently. Furthermore, as the AI model continues to learn and adapt, its productivity-enhancing capabilities will only increase over time, further benefiting the company’s operations.

If this still sounds too daunting and you are struggling to know where to start, use the contact form below to get in touch with one of Invero’s AI specialists.