Challenges Faced by Web3 Developers When Using Chatbots like ChatGPT
The use of chatbots, such as ChatGPT, has become increasingly popular among businesses of all sizes and industries. These chatbots offer a cost-effective and efficient way to enhance customer experience and streamline operations. In fact, experts predict that the chatbot market will reach a value of $2.3 billion by 2025, with a compound annual growth rate of 26.9% over the forecast period. It’s no wonder that chatbots are being utilized in various sectors including e-commerce, banking, finance, healthcare, and customer service, helping businesses save billions of dollars annually.
In the Web3 space, which involves constant distributed data computing demand, chatbots like ChatGPT play a crucial role in enhancing and streamlining development operations. However, there are several challenges that developers face when using ChatGPT without a predefined Web3 training model.
One of the major challenges is related to complex text-to-SQL translations. For example, if a Web3 developer gives ChatGPT a prompt that requires a complex translation, the chatbot may provide an inaccurate SQL response. This happens because ChatGPT is not well-versed in the developer’s project database and lacks knowledge about the schema cadence, primary keys, and foreign keys.
To address this challenge, two predominant datasets are involved in the NQL-to-SQL translation: WikiSQL, a large annotated corpus for building language interfaces, and Spider, a large-scale annotated semantic parsing and text-to-SQL dataset.
Currently, to train ChatGPT, a Web3 developer needs to enter the entire database in prompts. However, this approach requires a significant number of tokens, resulting in a high query processing cost for ChatGPT. Additionally, the cost calculation of ChatGPT’s latest version, GPT 4, is a significant challenge. ChatGPT charges a token for every 3-4 words a developer enters in their text query, making it costly for fully functional application development with large Web3 project databases.
To overcome these challenges, it is crucial to build pre-trained models that can easily understand the database schema cadence and be linked to the structure, primary key, foreign key, and schema of a Web3 project. Instead of repeatedly entering the database and schema codes and paying tokens for every few words, developers can fund a one-time chatbot training for Web3 development using an aggregated token cost.
By addressing the pragmatic concerns of ChatGPT, developers can build seamless and adaptive generative AI models that offer new potential for future dApp and Web3 advancements. The upgraded architecture of ChatGPT also supports multilingual programming languages for dApp development, showcasing its capacity to recognize and produce appropriate dApp code patterns.
In conclusion, while chatbots like ChatGPT are becoming increasingly popular in the Web3 space, developers face challenges when integrating these chatbots into Web3 systems. By focusing on resolving these challenges and optimizing the performance of chatbots, developers can unlock the full potential of generative AI models for Web3 and dApp advancements.
This article was written by Vinita Rathi, the Founder and CEO of Systango, specializing in Web3, Data, and Blockchain.
This article was published through Cointelegraph Innovation Circle, a vetted organization of senior executives and experts in the blockchain technology industry who are shaping the future through connections, collaboration, and thought leadership. Please note that the opinions expressed in this article do not necessarily reflect those of Cointelegraph.
