Companies have been using chatbot applications for years to provide responses to their users. While early adopters were limited to providing responses based on the information available to them at the time, foundational AI models like Large Language Models (LLMs) have enabled chatbots to also consider the context and relevance of a response. Today, AI-powered chatbots are able to make sense of information across almost any topic, and provide the most relevant responses possible.

With continued advancements in AI, companies can now leverage AI models built specifically for chatbot use cases — chatbot models — to automatically generate relevant and personalized responses. This class of AI is referred to as generative AI.

Generative AI has transformed the world of search, enabling chatbots to have more human-like interactions with their users. Chatbot models (e.g. OpenAI’s ChatGPT) combined with vector databases (e.g. Pinecone) are leading the charge on democratizing AI for chatbot applications, enabling users of any size — from hobbyists to large enterprises — to incorporate the power of generative AI to a wide range of use cases.

Chatbot use cases

Chatbots trained on the latest AI models have access to an extensive worldview, and when paired with the long-term memory of a vector database like Pinecone, they can generate and provide highly relevant, grounded responses — particularly for niche or proprietary topics. Companies rely on these AI-powered chatbots for a variety of applications and use cases.

Examples:

• Technical support: Resolve technical issues faster by generating accurate and helpful documentation or instructions for your users to follow.

• Self-serve knowledgebase: Save time and boost productivity for your teams by enabling them to quickly answer questions and gather information from an internal knowledgebase.

• Shopping assistant: Improve your customer experience by helping shoppers better navigate the site, explore product offerings, and successfully find what they are looking for.

Challenges when building AI-powered chatbots:

There are many benefits to chatbot models such as enabling applications to improve the efficiency and accuracy of searching for information. However, when it comes to building and managing an AI-powered chatbot application, there can be challenges when responding to industry specific or internal queries, especially at large scale. Some common limitations include:

• Hallucinations: If the chatbot model