The rapid growth of chatbots has made them a popular choice for businesses across various industries. These AI-powered tools offer cost-effective solutions to enhance customer experience and streamline operations. The chatbot market was valued at approximately $435.2 million in 2018, and experts predict it will reach $2.3 billion by 2025, indicating a compound annual growth rate (CAGR) of 26.9%. As a result, chatbots are widely employed in e-commerce, banking, finance, healthcare, and customer service.
However, when it comes to Web3 development, developers face unique challenges when using chatbots like ChatGPT. Chatbots play a crucial role in the Web3 space, which demands constant distributed data computing. Integrating an AI language model into Web3 operations offers numerous benefits but can encounter significant obstacles without a predefined training model.
Lack of Training Models
One key challenge is the lack of training models for ChatGPT in the context of Web3 development. When presented with a complex text-to-SQL translation prompt, ChatGPT struggles to accurately respond as it lacks knowledge about the developer’s project database schema, including primary and foreign keys. Existing datasets like WikiSQL and Spider are commonly used for NQL-to-SQL translation, but training ChatGPT requires developers to input the entire database in prompts, resulting in high query processing costs.
High Cost for Processing Queries
Another major challenge is the cost associated with processing queries in ChatGPT’s latest version, GPT 4. For every three to four words entered by a developer, ChatGPT charges a token. Considering the size of a complete Web3 project database, the token cost for one fully functional application development might exceed 1,000 tokens and even go up to 8,192-32,768 tokens.
Potential Steps to Mitigate These Challenges
To mitigate these challenges, AI developers should focus on building pre-trained models capable of converting text to SQL. Additionally, training chatbots to use the project database and business intelligence can help them understand the database schema cadence and accelerate Web3 code generation. Tailoring chatbots like ChatGPT to the specific structure, primary key, foreign key, and schema cadence of a Web3 project can reduce per-token costs.
By addressing the practical issues faced by ChatGPT, developers can create seamless and adaptive generative AI models that contribute to the advancement of Web3 and decentralized application (dApp) development. Upgrading the architecture of ChatGPT to recognize and produce appropriate Web3 and dApp code patterns, as well as supporting multilingual programming languages, will further enhance its capabilities.
Chatbots like ChatGPT are emerging as essential platforms for dApp development in the evolving Web3 technology landscape. While integrating chatbots into these systems poses challenges, resolving the pragmatic issues associated with ChatGPT opens up new possibilities for future dApp and Web3 advancements.
Vinita Rathi, the Founder and CEO of Systango, specializes in Web3, Data, and Blockchain technologies.
Note: This article was published through Cointelegraph Innovation Circle, an organization of senior executives and experts in the blockchain technology industry who foster connections, collaboration, and thought leadership. The opinions expressed do not necessarily reflect those of Cointelegraph.