There are so many AI tools available in the market. It all started with Chat GPT, and today we have so many competitive AI tools available, and each one is doing different things better. We can pick an AI tool that solves our needs and our problems. Some tools are good for writing code, and some are good for just ideation. Today, clients are able to share ideas with the help of these AI tools in a presentable manner, and at the same time, individuals are also able to present the ideas to others with the help of these great tools.
Even though there are several AI tools out there and it is still evolving, we cannot say that AI tools can be 100 percent accurate. They also make a lot of mistakes, and we have to be very careful in assessing what we get from them. Context retention and hallucination problem is a very common problem with AI tools, and we have to be clever enough to mitigate the risks due to this problem.
This is one of the biggest problems in AI. Especially if we are using any free AI tools, this is a big problem because if we keep feeding the AI with some set of information, it can retain the information and provide responses based on the information we provided, but there is a memory limit available, and beyond which the AI will start hallucinating. In modern tools, the context memory has improved a lot, and that's why some LLMs are able to read the code from a repository, keep it in memory, and provide prompt responses.
I have experienced this multiple times when working with AI. When I ask AI to generate a code, it gives me the code. Though most of it is correct, the follow-up questions become a problem. For the first few answers, it will be good, but as the context memory increases, the AI will start hallucinating and start acting weird. We might ask to change something, and it will end up changing what we asked, but at the same time, it will also change something that we did not ask for. This becomes a big mess after a huge context, and it becomes very hard to extract the work from AI after that.
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