Join us as we take a closer look at integrating Generative AI, like OpenAI’s ChatGPT models, into Conversational AI solutions for Enterprise Businesses. We highlight the benefits of Generative AI solutions, top business areas to leverage these solutions, risks to watch out for, and best practices when implementing these exciting new technologies.
Unlocking the Potential of Large Language Models in Conversational AI
Enterprise businesses can enhance customer experiences through the combined use of Natural Language Understanding (NLU) and Large Language Models (LLMs) to better understand, engage, and respond to customer needs, driving increased customer satisfaction, and brand loyalty.
This guide speaks mainly to publicly available as-a-Service LLM solutions (ex. ChatGPT, Bing, Bard) which are models trained on billions of words, phrases and text (text corpus) and are adept at a variety of NLP tasks, especially generating and classifying text which makes them suitable for use in common enterprise messaging and chatbot use cases.
In the future we’ll dig deeper into open source LLMs you can manage on-prem or in your cloud for those who require more domain specialization, control, choice, potentially lower cost, or for other reasons don’t want to rely on a 3rd party API service.
Benefits of Integrating Generative AI Solutions
- Improved Performance: Generative AI Chatbots and virtual assistants can understand and respond to a wider range of language inputs. This can lead to understanding customer intent faster, higher containment rates, and improved overall customer experiences.
- Hyper-personalized Conversational Commerce: Utilizing LLMs can help create increasingly customized marketing messages and product recommendations based on individual customer profiles and preferences, leading to higher engagement, better conversion rates, and improved CSAT.
- Enhanced Search Functionality: Enhancing the search experience on websites, knowledge bases, and apps with the help of Generative AI solutions. Employing NLU and LLMs in search allows a user to more easily describe what they are looking for, as user intent is understood faster, increasing the likelihood of delivering accurate and relevant search results.
- Multilingual Conversational Commerce: Expanding global reach and adding new market segments. Using NLU and LLMs allows for the quick training and tuning of additional languages, making it easier to offer multilingual customer support and conversational commerce solutions.
- Advanced Analytics: Leveraging NLU and LLMs to analyze large volumes of unstructured data, such as customer interactions, to identify new intents, sentiment, trends, patterns, and potential areas of improvement throughout the customer journey.
Thinking of incorporating Generative AI into your existing chatbot? Validate your idea with a Proof of Concept before launching. At Master of Code Global, we can seamlessly integrate Generative AI into your current chatbot, train it, and have it ready for you in just two weeks.
Top 5 Business Areas to Integrate Generative AI Solutions
- Customer Support: Integrate Generative AI into your support stack that is specialized in your specific domain so that it can summarize customer interactions, group similar cases, and surface contextualized knowledge for agents, reducing customer escalations.
- Chatbots and Virtual Assistants: Enhance containment and CSAT of existing solutions by allowing customers to more easily self-serve through the use of Generative AI. Whether over the phone (using natural language) or via chat, virtual agents can now more easily converse and understand intents in dozens of languages.
- Sales & Marketing: Quickly create highly personalized and usable content, helping sales and marketers target specific ICPs through email, presentations, brochures, and social media posts.
- Customer Success: Use Generative AI solutions to create quick and efficient responses to customer issues, reducing the number of steps to reconcile requests or even fully automating existing use cases that used to require a human touchpoint. A/B test the use of different email writing styles to address a customer concern, handle a sensitive commercial topic, or break bad news.
- Workflow Automation and Optimization: Document processing, automated email classification and response, meeting scheduling and calendar management, knowledge base creation and management, task management and prioritization, data analysis and reporting, employee training and development – the options are endless.
Check out even more insightful ChatGPT and Generative AI statistics for business.
Risks and Mitigation Strategies of Generative AI Solutions
The risks of using Generative AI solutions have been widely discussed. Here are some key areas and suggestions to minimize potential risks.
LLM hallucinations – Generative AI service output is not necessarily based on facts or reality and should be carefully considered depending on the use case. You can employ several tactics to help tame its wild side, including:
- Limiting response length: Restricting the length of the generated response to minimize the chance of irrelevant or unrelated content.
- Controlled input: Rather than offering a free-form text box for users, suggest several style options to act as guide rails. For example, ask the user if the email they want to create should be 1. thankful, 2. remorseful, or 3. empathetic.
- Adjusting temperature: Controlling the randomness of the output by adjusting the temperature parameter.
- Using a moderation layer: Filtering out inappropriate, unsafe, or irrelevant content before it reaches the end-user.
- Implementing user feedback loops to instruct the model on what it has done right or wrong, enabling it to adjust parameters to perform better in the future.
- Fine-tuning the model: Improving the performance of the LLM model solution by fine-tuning it using a domain-specific dataset to reduce the likelihood of hallucinating answers.
Unintended bias – Bias is present everywhere, and LLM solutions have been trained on available (biased) data available on the internet. Mitigating this risk is crucial to ensure fairness, safety, and inclusion. Possible strategies could be:
- Fine-tuning the model: Minimizing inherent biases by fine-tuning the LLM model(s) using a diverse and representative dataset.
- Adding Bias-detection tools: Identifying and flagging biased outputs using bias-detection tools that can be rule-based systems, machine learning models, or a combination of both.
- Implementing a moderation layer: Validating information received from a LLM by an external NLP system, for example, and then sending it to the end-user.
- Adding a user feedback loop with unbiased, external information from a source that is not strongly correlated to the outputs of the model.
- Collaborating with diverse teams: Working with a diverse team of experts, including ethicists, social scientists, and domain experts, to gain insights into potential biases and develop strategies to mitigate them.
- Providing iterative improvements so that task requirements are addressed and the appropriate level of quality is attained.
Over-reliance on AI and loss of human touch – By carefully combining AI with human agents you can achieve optimal results. The goal here is to identify when the assistance of a real agent is required, not just to facilitate a seamless conversation handoff, but also to prepare the agent with conversation context, escalation reason, and sentiment analysis. This information will enable the human agent to understand the user’s problem faster and avoid having to ask the customer to repeat information.
Leveraging Data Sets and Best Practices for Implementation
When the domain information is mostly public and has likely already been included in public model training data, such as a website or code documentation page, we can use a LLM like the GPT-3.5 Turbo model and OpenAI Chat Completion API – along with injections when we need to extend the context of the conversation with new information.
When the domain information is not present in the public model training data, and we have a large amount of unstructured information the model should be aware of, we can use the OpenAI Embeddings API – which creates embedding vectors from the text data.
We use a vector database such as Pinecone – to search for relevant answers, and based on that search, we dynamically build a GPT prompt engineering for text completion or the chat completion API.
When domain information is not public and cannot be exposed publicly, and such information is well-structured, we can use the Curie model fine-tuning – and employ the text completion API afterward to obtain relevant answers based on this dataset.
Interoperability of Generative AI Solutions with Existing Chatbots and AI Systems
While LLMs like ChatGPT have transformed conversational app development with its human-like interactions, traditional NLP and flow approaches remain efficient for specific use cases, such as transactional flows like payments, updating information in CRMs, and calendars.
Many enterprise businesses have already invested in their conversational app infrastructure based on traditional NLP frameworks but are interested in experimenting with Generative AI solutions and large language models. Instead of abandoning their legacy systems, they want to incorporate LLMs into their existing bots.
To address this, we have developed a middleware that combines flow-based NLP approaches with an embedded Generative AI solution powered by OpenAI’s GPT 3.5 Turbo model. It sends additional context to the language model-based bot while escalating from the legacy system and provides additional parameters, such as intent, sentiment, and entities, while escalating back from the Generative AI-based flow. This allows us to easily incorporate Generative AI experiences into existing flows.
Don’t miss out on the opportunity to see how Generative AI chatbots can revolutionize your customer support and boost your company’s efficiency.