Maximizing the Performance of an OpenAI Model-Based Assistant
Creating an assistant based on the OpenAI model requires a specific approach to maximize the performance of the results. To achieve 100% accuracy, certain steps need to be taken in addition to simply creating a chatbot that can respond correctly in 80% of cases.
First and foremost, having good quality data is crucial. The data should be readable and well-structured, covering the targeted expertise. Often, clients neglect these basic prerequisites, according to Thomas Sabatier, CEO of tolk.ai, a French no-code bot GPT development platform.
Next, a structured information pipeline needs to be created. This involves developing a model that can generate questions based on the text and segmenting paragraphs. Identifying keywords and summarizing the content of each paragraph is also essential. This process optimizes information retrieval when a question is asked in order to provide the correct answer.
Structuring the content into sections and summarizing them helps reduce the risk of false answers or hallucination. This approach is important when using other large language models like Falcon or Claude 2.
Additionally, establishing a confidence score for ranking the answers further refines the accuracy of the assistant. By combining these methods, an insurer, for example, can create the equivalent of a ChatGPT that can answer questions about policies, terms and conditions, and coverage details.
Prompt engineering is another crucial aspect. It involves providing instructions to the model to generate the desired response, including the tone, precision level, length, structure, and style. This ensures that the bot’s “personality” aligns with the brand’s universe.
Using GPT has the advantage of being compliant with the General Data Protection Regulation (GDPR), making it an ideal choice. Microsoft Azure, in partnership with OpenAI, is the only provider that offers both high-performance capabilities and data hosting within the European continent.
To further ensure compliance with the GDPR for organizations in regulated sectors, implementing query pseudonymization is recommended. This eliminates any risk regarding data privacy. It’s important to note that GPT is a cloud solution based in the United States, whether operated by OpenAI or Microsoft on Azure.
Prior to the GPT model, an NLP engine serves as an orchestrator, managing interactions with the bot. It handles capturing, qualifying, and filtering user intentions before guiding them accordingly. It determines whether the general-purpose GPT model can answer the question or if a specialized version would be more appropriate. It also considers whether the conversation should be escalated to a human or if it requires submission through a form or support tunnel.
The orchestrator also interfaces with the organization’s information system, such as product databases, order tracking, and ticketing tools to ensure seamless customer support. Additionally, it includes a calculation engine, as GPT-4 may not be sufficient for complex calculations. Finally, the NLP engine can handle critical questions that require validation from a human.
By following these steps, the performance of an assistant based on the OpenAI model can be maximized, providing accurate and reliable responses to user queries.
Sources:
– Thomas Sabatier, CEO of tolk.ai
– Louis-Clément Schiltz, Founder and CEO of Webotit.ai
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