In the last 10 years, AI technology has boomed. It has become more and more accessible and many people now use it in their daily life. Have you ever w ondered why people can achieve greatness using it, but others seem to struggle to come up with something good?
When developing a new AI powered application, the need to provide external knowledge to AI models is often required. They certainly have their own kno wledge, which they have been trained on, but sometimes, the information they need to process the right answer might be hidden from public knowledge or situational to the task at hand. In those cases, relevant data needs to be provided with the user’s query to increase your chances of success.
Talking with AI models such as ChatGPT or Claude seems easy. You would think conversations would be similar to ones between two people, discussing a t opic or providing instructions about a task to be done. Unfortunately, it isn’t always the case.
With the accessibility of LLMs to the public, it has become widely popular to integrate chatting models into websites. We are on the verge of a revolu tion in the way users consume the web and it might become crucial in the near future to harness the power of artificial intelligence into new and existing products.
Since the release of Claude 3, the AI model has boomed in popularity and became a serious alternative to ChatGPT. The quality of its reasoning and the relevance of its answers made it a perfect choice as an engine to drive an AI powered application forward.
Since AI models are designed to react their own way when given a user input, they tend to be unpredictable. This unpredictability can lead to emergent situations for both the users and the developers but can also expose security flaws, if mishandled.
By using AI code assistants, the key to success lies in the interactions between the tool and the programmer. As the name implies, the "assistants" ar e there to take instructions and convert them into workable code for you. For this to happen, you have to know what needs to be coded in the first place.
It was only a matter of time before artificial intelligence technology’s power was to be harnessed towards processing programmable language. The log ical reasoning of AI mixed with the immense pool of open source examples paved the way to the creation of the first stepping stone in an automated digital product creation environment.
In an era where AI technology is getting integrated in many aspects of our lives, being able to properly externalize what we need is a must. The time when AI models can read our brain to know exactly what we want hasn’t come yet and it’s crucial to be able to craft meaningful prompts to achieve the task at hand.
In our day and age, people are getting easier access to information, now more than ever. With the coming of the internet, search engines, social media and artificial intelligence, almost anything can be found in a mere couple of seconds.
