The Reliability of ChatGPT in Answering Complex Technical Questions: An In-Depth Analysis

When it comes to technical questions, the reliability of AI tools like ChatGPT has been a subject of considerable debate. This article explores the accuracy and effectiveness of ChatGPT in responding to complex technical queries, drawing from practical experiences and recent research.

Accuracy and Reliability in Technical Queries

Recent studies and user experiences suggest that the accuracy of ChatGPT in handling technical questions, particularly those involving database operations such as MySQL stored procedures, is on par with other popular search engines. However, several factors can affect the reliability of its responses. Unlike a traditional search engine where users get a list of potentially accurate sites, ChatGPT provides direct answers. These answers can be misleading, especially when dealing with intricate or nuanced topics like database processing.

A process involving multiple queries often becomes necessary to ensure the accuracy of the response. Users must validate answers and refine their queries to drive ChatGPT closer to the correct solution. This iterative approach can be time-consuming and demanding, especially for users who are not familiar with the intricacies of the technology being discussed.

Case Study: MySQL Stored Procedure

For instance, a user asked for a MySQL stored procedure to handle a list of ImageNames separated by tab characters. The initial response from ChatGPT utilized features the user was not familiar with, which worked the first time but had restrictions in processing more than five names. Subsequent attempts to improve the procedure faced several issues, including compilation errors. It took five iterations to arrive at a final, simplified version that the user found easier to understand and implement.

Initially, the first result was:

The result used features I did not realize existed worked first time except it could only process list up to 5 names long.

Following feedback, the second response was:

I pointed this out and it then gave me an improved SQL procedure but the improved one failed to be created/ compile.

On the third attempt, the response was:

Fed back error message and it gave me back one with a fix that also failed.

The fourth attempt was promising but still felt overly complex:

4th version worked but seemed more complicated than it needed to be.

Finally, the fifth iteration addressed the user's concerns, proposing a simpler solution using a while loop with LOCATE and SUBSTR functions, which ultimately worked:

Asked if it would be simpler to parse ImageNames in a while loop using LOCATE and SUBSTR to consume string. It said yes then produced the finale version.

This case study highlights the iterative nature of working with AI models like ChatGPT. It also underscores the importance of user validation and feedback to refine the results and achieve the desired outcome.

Evaluation of AI Models

According to a CNN article published on August 29, 2023, AI models are designed to provide “plausible-sounding” answers. However, research shows that AI responses frequently contain completely fabricated or false information. This tendency is often referred to as “hallucinations” or “confabulations”, as noted by Meta’s AI chief in a tweet. Some social media users have even labeled chatbots as “pathological liars”, emphasizing the unreliability and potential for misinformation.

These findings suggest that users should approach AI-generated technical answers with caution. Always validate the information provided by AI with known sources and practical testing. Additionally, users should consider the context and the complexity of the question when interpreting AI responses.

Conclusion

While AI tools like ChatGPT offer a convenient and efficient way to explore complex technical questions, their reliability can be affected by multiple factors. Unlike traditional search engines, which provide a breadth of information from various sources, ChatGPT offers direct but potentially incorrect responses. Iterative refinement through user feedback is necessary to achieve accurate and reliable results.

To maximize the benefits of AI tools in technical problem-solving, users should critically evaluate AI-generated answers, seek corroborating evidence, and apply practical testing. In doing so, they can harness the power of AI while minimizing the risk of incorporating inaccurate or misleading information into their work.