Challenges Web3 developers encounter when using chatbots like ChatGPT
Chatbots have become all the rage for businesses of all sizes and industries as they offer a cost-effective and efficient way to improve customer experience and streamline operations.
Did you know the chatbot market was worth around $435.2 million in 2018? Experts predict that the chatbot market will reach $2.3 billion by 2025. That’s a compound annual growth rate (CAGR) of 26.9% over the forecast period. It’s astonishing to see how quickly the chatbot market is growing.
It’s no wonder that chatbots are increasingly used in e-commerce, banking, finance, healthcare and customer service. Its usage has helped businesses save over $8 billion annually in e-commerce and reduced customer service costs by up to 30%.
So, if you still haven’t jumped on the chatbot bandwagon, it could be high time you consider exploring the possibilities.
Chatbots like ChatGPT play a dynamic role in the Web3 space (which has a constant distributed data computing demand). In that context, it is crucial to understand the value of using an AI language model to enhance and streamline Web3 development operations.
However, without a predefined Web3 training model, ChatGPT would face some significant challenges. For instance, consider a scenario where a Web3 developer gives ChatGPT a prompt that requires a complex text-to-SQL translation.
ChatGPT is not well-versed in the developer’s project database and cannot map the NQL logic to SQL response. It provides an inaccurate SQL response to the Web3 developer’s prompt. This happens because it does not know about the schema cadence and primary and foreign keys of the developer’s project database.
There are two predominant datasets involved in the NQL-to-SQL translation. One is WikiSQL (a large annotated corpus
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