So, if you haven’t already joined the chatbot revolution, it might be time to think about doing so.Due to their ability to improve customer experience and manage operations at a low cost, chatbots have become extremely popular among businesses of all sizes and in all sectors.
Did you know that the market for chatbots was valued over $435.2 million in 2018? The market for chatbots is expected to grow to $2.3 billion by 2025, according to experts. In terms of the anticipated period, that represents a CAGR of 26.9%. The market for chatbots is expanding so quickly, it’s incredible.
It makes sense that chatbots are being utilised more frequently in customer service, banking, and other financial and business-related functions. Through its use, firms have been able to cut their e-commerce spending by over $8 billion yearly and up to 30% off the cost of providing customer care. As a result, if you haven’t already joined the chatbot revolution, it could be time to do so.
In this article we’ll talk about the major challenges of using chatbots as well as how to mitigate those challenges.
Major obstacles encountered using chatbots like ChatGPT,
In the Web3 arena (where there is a constant need for distributed data computation), chatbots such as ChatGPT play a dynamic role. It is critical to comprehend the benefits of utilising an AI language model to improve and coordinate Web3 development activities in this environment.
ChatGPT would, however, have some serious difficulties without a specified Web3 training model. Consider a situation where a Web3 developer sends ChatGPT a command that necessitates a challenging text-to-SQL translation.
1. Inadequate training models: ChatGPT is unfamiliar with the developer’s project database and is unable to translate NQL logic into a SQL response. In response to the Web3 developer’s prompt, it gives an incorrect SQL result. This occurs because it is unaware of the primary and foreign keys, schema cadence, and project database of the developer. In the NQL to SQL translation, there are primarily two datasets used. One is WikiSQL, a sizable corpus of annotated text for creating language interfaces, and the other is Spider, a sizable dataset of annotated semantic parsing and text-to-SQL.
The underlying database schema cadence should now be understood by chatbots like ChatGPT, who should also become acclimated to the new schemas. Currently, to accomplish this, a Web3 developer must train ChatGPT by entering the complete database. A certain amount of tokens are needed to train data models through prompts, which results in significant query processing costs for ChatGPT.
2. High cost of query processing: The cost estimation of ChatGPT’s most recent version, GPT 4, presents yet another big obstacle. ChatGPT charges a token for every 3–4 words a developer types in a text query for SQL. Therefore, taking into account the size of a full Web3 project database, it may cost more than 1,000 tokens (and as much as 8,192–32,768 tokens) to construct a single completely functional application.
According to Julian, a co-founder of the cryptocurrency aggregator Mobula, ChatGPT is a revolutionary tool for Web3 innovation. It does not, however, have the technological capability to develop and expand a specific Web3 project.
Potential steps to mitigate these challenges
AI developers should pay particular attention to building large language models that have already been trained and can transform text to SQL.
Practically speaking, creating pre-trained models is still a key component of chatbot development. We must instead teach the chatbots how to use the project database and business intelligence (BI) in order for them to develop on their own. This training will facilitate chatbots’ comprehension of the database schema cadence and hasten the production of Web3 code. When customised and connected to the database structure, primary key, foreign key, and schema cadence of a Web3 project, a chatbot like ChatGPT can lower the cost per token.
A token is required every three to four words, so avoid repeatedly entering the database and schema codes. Instead, pay for a one-time chatbot development training session using aggregated token cost.
In conclusion, With the growth of Web3 technology, chatbots like ChatGPT are emerging as a crucial platform for dApp development. Developers do encounter a few roadblocks when adding chatbots to these systems, though.
By modernising the ChatGPT architecture, we can demonstrate the model’s ability to identify and generate relevant Web3 and dApp code patterns. For the creation of dApps, it also supports multilingual programming languages.
In order to create seamless and flexible generative AI models that offer new potential for future dApp and Web3 breakthroughs, we must first address the practical problems with ChatGPT.