Chatbots have become a hit with 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 that the chatbot market is worth it? $435.2 million in 2018? Experts predict that the chatbot market will reach $2.3 billion by 2025. This is a compound annual growth rate (CAGR) of 26.9% during the forecast period. It’s amazing to see how fast the chatbot market is growing.
It’s no wonder that chatbots are increasingly being used in e-commerce, banking, finance, healthcare and customer service. It has its uses he helped businesses save more than $8 billion annually in e-commerce and reduce customer service costs by up to 30%.
So if you still haven’t jumped on the chatbot bandwagon, it might be high time to consider exploring the options.
The real challenges of working with Chatbots like ChatGPT
Chatbots like ChatGPT play a dynamic role in the Web3 space (which has constant demands for distributed data). In this context, it is critical to understand the value of using an AI language model to improve and streamline Web3 development operations.
However, without the predefined Web3 training model, ChatGPT would face some significant challenges. For example, consider a scenario where a Web3 developer provides ChatGPT with a challenge that requires 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 NQL logic to SQL response. It provides an imprecise SQL response to a Web3 developer prompt. This happens because it doesn’t know about the schema cadence and primary and foreign keys of the developer’s project database.
Two sets of data predominate plugged in in NQL to SQL translation. One is WikiSQL (a large annotated corpus for building language interfaces) and the other is Spider (a large annotated semantic analysis and text-to-SQL dataset).
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Now a chatbot like ChatGPT should understand the basic cadence of the database schema and get used to the new schemas. Currently, to achieve this, the Web3 developer enters the entire database into the ChatGPT training calls. Training data models through challenges requires a certain number of tokens, resulting in a huge query processing cost for ChatGPT.
Challenge 2: High query processing costs
Another significant challenge is the costing of the latest version of ChatGPT GPT 4. For every 3-4 words a developer enters into their text query in exchange for SQL, ChatGPT charges a token.
Given the size of the complete database of the Web3 project, this could cost more than 1000 tokens (it can also reach 8,192-32,768 tokens) for the development of one fully functional application.
As established from Mobula (crypto aggregator) co-founder Julian, ChatGPT is a revolutionary tool for Web3 innovation. However, it lacks the technical potential to build and develop a specific Web3 project.
Potential steps to mitigate these problems
Creating huge language models that have already been trained and can translate text into SQL is something that AI developers should pay close attention to.
Pragmatically speaking, building pre-trained models remains a significant step in inventing chatbots. Instead of chatbots developing themselves, we will have to teach them to use the project database and business intelligence (BI). This training will make it easier for chatbots to understand the database schema cadence and speed up the creation of Web3 code.
A chatbot like ChatGPT can reduce the cost of a token if it is customized and linked to the database structure, primary key, foreign key and cadence of the Web3 project schema.
Avoid repeatedly entering database and schema codes and paying a token for three to four words. Instead, use the aggregated token cost to fund one-time chatbot training for Web3 development.
A final note
Chatbots like ChatGPT are emerging as an integral platform for dApp development with the evolving Web3 technology. However, developers face some ground barriers when integrating chatbots into these systems.
By upgrading the ChatGPT architecture, we can demonstrate the model’s ability to recognize and generate appropriate Web3 and dApp code patterns. It also supports multilingual programming languages for dApp development.
Thus, by solving the pragmatic problems of ChatGPT, we can create seamless and adaptive generative AI models that offer new potential for future dApp and Web3 enhancements.
Vinita Rathi is the founder and CEO of Systango, a company specializing in Web3, Data and Blockchain.
This article was published via the Cointelegraph Innovation Circle, a trusted organization of blockchain technology industry leaders and experts who are building the future through the power of connection, collaboration and thought leadership. The views expressed do not necessarily reflect those of Cointelegraph.
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