Introduction
Friends, today I want to share a particularly interesting experience with you. As a tech blogger who has been deeply involved in the AI field for many years, I recently received a heavyweight project - conducting generative AI technology selection for a large enterprise. To be honest, this project was quite overwhelming at first. However, during the process, I unexpectedly discovered an incredibly helpful "assistant" - ChatGPT. This experience was so amazing that I feel it's necessary to share it with you in detail.
The Challenge
Speaking of technology selection in large enterprises, it's truly a challenging task! Especially now that generative AI technology is flourishing, with major tech companies going all out in this field. Just as OpenAI releases a new product, Anthropic follows with an upgrade, and Google isn't falling behind, throwing out new technical solutions one after another. Every company is vigorously promoting how great their products are, honestly, even as a veteran in the industry, I found it dizzying.
When I first received this project, the client's requirement was deceptively "simple": within one week, provide a comprehensive selection report. This report needed to cover in-depth evaluations of all mainstream generative AI products in the market, from performance metrics to cost calculations, from security ratings to practical application effects - everything had to be thorough. I felt immense pressure at the time. It wasn't just like finding a needle in a haystack - it was like finding a specific needle in the ocean!
Moreover, the most difficult part of technology selection is that you can't just look at surface data. For instance, with performance evaluation, looking at official data alone is far from enough - you need to consider performance in actual application scenarios. Similarly, for cost assessment, besides the obvious API call fees, there are many hidden costs that need careful calculation. All of these require extensive research and verification work.
I remember one night, I was struggling with a screen full of product parameters. Technical documentation, evaluation reports, and user feedback were piled up like a small mountain - just organizing these materials would take me several days. Most troublesome was that much of the information was contradictory - some praised this product, others praised that one, leaving me confused about whom to believe.
The Inspiration
Just when I was about to be overwhelmed by these complex materials, I had a sudden inspiration. I thought: since I'm evaluating AI products, why not let AI help me with this evaluation? Especially ChatGPT - isn't it like a talking AI encyclopedia? Shouldn't it be familiar with all these AI products?
To be honest, when this idea first came to me, even I thought it was a bit far-fetched. But thinking again, ChatGPT's knowledge base does indeed cover vast amounts of technical documentation and evaluation materials, and its analytical capabilities are quite impressive. Most importantly, I was already at a dead end, so what harm could there be in trying?
So, I opened ChatGPT and began a fascinating journey of exploration. Honestly, I was a bit nervous at the time, worried that this idea might turn into a joke. However, what happened next really opened my eyes.
Implementation
I first tentatively asked ChatGPT to help me organize the current mainstream generative AI products in the market. Surprisingly, it not only provided a detailed list but also proactively helped me categorize them. In the text generation field, it thoroughly analyzed the characteristics of top-tier models like GPT-4, Claude 2, and PaLM 2.
For instance, regarding GPT-4, ChatGPT told me it excels particularly in complex reasoning and creative writing, though with higher API call costs. Claude 2 stands out in long-text processing and professional domains, with solid ethical boundary control. As for PaLM 2, it has unique advantages in multilingual processing and knowledge integration.
What delighted me more was that ChatGPT didn't simply list these features but analyzed them in relation to specific application scenarios. For example, when I mentioned the company planned to apply AI in their customer service system, it focused on analyzing how each model performs in terms of conversation fluency, context understanding, and multi-turn dialogue.
In cost analysis, ChatGPT's advice was exactly what I needed. It reminded me that when evaluating AI product costs, we can't just look at the surface-level API call fees. We need to consider hardware investments for model deployment, daily operation and maintenance labor costs, data security protection expenses, and so on. These are all unavoidable cost items in practical applications.
It even helped me create a detailed cost estimation table, clearly calculating both fixed and variable costs. For instance, when evaluating API call costs, it considered not only the basic call fees but also network transmission, data storage, disaster recovery backup, and other related expenses. These details were indeed things I had previously overlooked.
Unexpected Gains
During the entire exploration process, I found that ChatGPT could not only provide ready-made answers, but more valuably, its analytical approach. Whenever I posed a question, it would discuss it from multiple dimensions, helping me comprehensively understand various aspects of the problem.
For example, when discussing AI product security, ChatGPT would analyze from multiple angles including data privacy protection, model protection mechanisms, and compliance requirements. It told me that when enterprises choose AI products, they need to pay special attention to data processing procedures. Some models, despite their strong performance, might save training data, which could be a potential risk for enterprises with high data security requirements.
Additionally, ChatGPT would proactively remind me of easily overlooked technical details. For instance, when discussing API integration solutions, it reminded me to consider network latency issues in different regions. For application scenarios requiring real-time response, this factor could directly affect user experience. These detailed suggestions are all very crucial in practical applications.
Once, when we were discussing model scalability, ChatGPT raised an interesting point. It said many enterprises often focus only on current needs when choosing AI models, neglecting future expansion possibilities. It suggested that the evaluation should consider factors like model iteration cycles and upgrade costs. This advice was indeed forward-thinking.
Regarding performance testing, ChatGPT also provided many practical suggestions. It reminded me that looking at officially published performance metrics alone isn't enough - it's better to design test cases that are close to actual application scenarios. For example, if it's used for a customer service system, we should test how the model performs when handling dialects, technical terms, and ambiguous statements.
Experience Summary
Through this practice, I summarized several experiences in using ChatGPT for technology selection assistance. These experiences are not only applicable to AI product selection but also valuable for other technology selection work.
The first point is to be good at asking questions. Rather than letting ChatGPT give general answers, it's better to guide it to conduct targeted analysis. For example, I would ask: "For an enterprise with 1 million daily API calls, which model would be most suitable? Considering cost-effectiveness, how should we balance performance and budget?" Such specific questions are more likely to get valuable answers.
In practice, I found that the way questions are asked directly affects the quality of answers. If the question is too broad, like simply asking "Which AI model is the best?", the answer tends to be general. But if you can clearly state specific application scenarios, business requirements, and technical constraints, ChatGPT can provide more targeted suggestions.
The second point is the importance of cross-validation. Although ChatGPT provides professional information, to ensure accuracy, I would still ask it to list information sources and then verify them against official documentation. While this process takes extra time, it greatly improves the reliability of selection results.
Moreover, through cross-validation, I sometimes discover new information that ChatGPT hadn't mentioned. This information often becomes new discussion points, helping us further deepen our analysis. During the verification process, I also developed a good habit: recording important information to form my own knowledge base.
The third point is to treat ChatGPT as a discussion partner. I found brainstorming with it particularly effective, as it often provides unexpected perspectives. Sometimes when I thought a solution was perfect, ChatGPT might point out potential issues I hadn't considered, which helps improve the completeness of the solution.
Throughout the selection process, I found that ChatGPT isn't just an information provider but more like an experienced technical consultant. It can provide reasonable suggestions based on existing knowledge combined with specific scenarios. Sometimes its perspectives might seem surprising at first but make perfect sense upon reflection.
Conclusion
This technology selection experience has given me a deeper understanding of AI technology. AI is not just the object of our selection but can become a powerful assistant in the selection process. It can help us clarify thoughts, expand perspectives, and discover blind spots. This experience truly feels magical and has increased my confidence in the future development of AI technology.
By the way, I'd particularly like to hear your thoughts. Have you used similar methods when doing technology selection? Or do you have any unique selection experiences to share? Welcome to tell me in the comments section, let's discuss and grow together.
Honestly, through this experience, I deeply realized that technology selection isn't a solo battle. Being able to make good use of tools and leverage AI's power often leads to twice the results with half the effort. I hope my shared experiences can be helpful to you, and I look forward to hearing your stories and insights.
