Challenges faced by Web3 developers using chatbots like ChatGPT.
Chatbots have become extremely popular among businesses of all sizes and industries because they provide a cost-effective and efficient way to enhance customer experience and streamline operations.
Did you know that the chatbot market was valued at approximately $435.2 million in 2018? Experts predict that the market will reach $2.3 billion by 2025, indicating a compound annual growth rate (CAGR) of 26.9% over the forecast period. It’s astonishing to witness the rapid growth of the chatbot market.
It’s no surprise that chatbots are increasingly utilized in e-commerce, banking, finance, healthcare, and customer service. Their implementation has helped businesses save over $8 billion annually in e-commerce and decrease customer service costs by up to 30%.
Therefore, if you haven’t yet embraced chatbots, now might be the perfect time to explore the possibilities.
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Real challenges while engaging with Chatbots like ChatGPT
Chatbots like ChatGPT play a dynamic role in the Web3 space, which involves constant distributed data computing demand. In this context, it is crucial to recognize the value of utilizing an AI language model to enhance and streamline Web3 development operations.
However, without a predefined Web3 training model, ChatGPT would face significant challenges. For example, consider a scenario where a Web3 developer gives ChatGPT a prompt that requires a complex text-to-SQL translation.
Challenge 1: Lack of training models
ChatGPT is not well-versed in the developer’s project database and cannot map the NQL logic to SQL response accurately. It provides an incorrect SQL response to the Web3 developer’s prompt. This happens because it is unaware of the schema cadence and primary and foreign keys of the developer’s project database.
Two predominant datasets are involved in the NQL-to-SQL translation: WikiSQL, which is a large annotated corpus for building language interfaces, and Spider, which is a large-scale annotated semantic parsing and text-to-SQL dataset.
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Now, a chatbot like ChatGPT should understand the underlying database schema cadence and become familiar with the new schemas. Currently, a Web3 developer has to input the entire database in prompts to train ChatGPT. Training data models through prompts require a certain number of tokens, resulting in significant query processing costs for ChatGPT.
Challenge 2: High cost for processing queries
Another significant challenge is the cost calculation of ChatGPT’s latest version, GPT 4. For every 3-4 words a developer inputs in their text query for SQL, ChatGPT charges a token.
Therefore, considering the size of a complete Web3 project database, it could cost more than 1,000 tokens (or even as high as 8,192-32,768 tokens) for the development of a fully functional application.
According to Julian, the co-founder of Mobula (a crypto-aggregator), ChatGPT is a revolutionary tool for innovation in Web3. However, it lacks the technical potential to build and grow a specific Web3 project.
Potential steps to mitigate these challenges
Building large language models that are already trained and capable of converting text to SQL is something AI developers should focus on.
Practically speaking, building pre-trained models remains a significant step in chatbot development. Instead, for chatbots to evolve on their own, we must teach them how to utilize the project database and business intelligence (BI). This training will enable chatbots to better understand the database schema cadence and expedite the creation of Web3 code.
A chatbot like ChatGPT can reduce the cost per token by tailoring it to the database structure, primary key, foreign key, and schema cadence of a Web3 project.
Avoid repeatedly inputting the database and schema codes and paying a token for every three to four words. Instead, utilize aggregated token costs to fund one-time chatbot training for Web3 development.
Endnote
Chatbots like ChatGPT are becoming increasingly important in dApp development using Web3 technology. However, developers may encounter some challenges when integrating chatbots into these systems.
We can demonstrate the model’s ability to recognize and generate appropriate Web3 and dApp code patterns by enhancing the ChatGPT architecture. It also supports programming languages for dApp development in multiple languages.
By addressing the practical issues of ChatGPT, we can create seamless and adaptable generative AI models that offer new possibilities for future advancements in dApp and Web3.
Vinita Rathi is the Founder and Chief Executive Officer of Systango, specializing in Web3, Data, and Blockchain.
This article was published through Cointelegraph Innovation Circle, a verified organization of senior executives and experts in the blockchain technology industry who are shaping the future through the power of connections, collaboration, and thought leadership. The opinions expressed do not necessarily reflect those of Cointelegraph.
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