With the age of artificial intelligence advancing by leaps and bounds, we find ourselves with increasingly sophisticated tools and techniques that allow us to interact with and get the best out of these systems. Today we will delve into one of these techniques, known as the thought chain in the field of Prompt Engineering, and how it has revolutionized the way we get answers from artificial intelligence models such as GPT.
What is the chain of thought in Prompt Engineering?
The chain of thought is a methodology that allows guiding an artificial intelligence model through a detailed logical process, so that it can reach a conclusion or solve a complex task. This technique became popular following the publication of the Google team's work on their language model called Palm.
Example of a chain of thought
Imagine that you want an AI model to solve a mathematical problem involving several steps. We can present you with the scenario in detail, so that the model can follow each step and arrive at the correct solution. For example, if we ask it to calculate how many tennis balls Roger has after buying two more boxes, each with three balls, starting with five, this might confuse a model without context. But by specifying a sequence of steps at the prompt, the model can sequentially process the problem and arrive at the correct answer.
Why is context important in AI prompts?
The importance of context lies in the limited nature of AI models. They often lack the ability to understand the intent behind a question or the complex structure of a problem without adequate context. Through the thought chain, we give the AI the necessary context so that it can "think" in a structured way and arrive at the appropriate solution.
How to apply a chain of thought with artificial intelligence?
Step 1: Identify the scenario
Start by describing the problem, scenario or question to the AI in a question and answer (Q&A) format. This will also include the initial variables or data from the problem that the AI needs to understand to move forward in the thought chain.
Step 2: Establish the relationship between the elements.
The next step is to have the AI understand the connection between the various parts of the problem, evaluate the relationships, and proceed to analyze based on the scenario provided.
Step 3: Move towards the solution
Once the AI has evaluated the information from the previous step, it will determine a possible solution based on the analysis and provide a coherent answer.
Step 4: Verify and adapt
Finally, verify the model's response to make sure it fits the logic and, if necessary, adjust the prompts to refine the AI's thought process.
How to avoid "hallucinations" in AI models?
Hallucinations refer to occasions in which an AI model produces answers with high confidence, but which are incorrect or do not make sense. To mitigate this phenomenon, it is critical to break down the problem into steps that the AI can logically follow and provide clear examples of thought structures that help the AI stay on track.
What is it and how does the zero-shot chain of thought work?
Sometimes an AI model can be induced to perform a thought process without the need to specify each step in detail. This is called a zero shot chain of thought. While it can be effective, this approach does not always provide reliable results, so its use should be carefully considered and adjusted as needed.
Recognizing the power of the chain of thought in Prompt Engineering, this technique becomes indispensable for solving specific or complex problems, adapting to the desired level of abstraction. It is key in developing efficient and effective solutions in the field of artificial intelligence. Let us be inspired by exploring the limits of these tools, always confident that, step by step, even the most complex challenges can be addressed. Let us continue to learn and apply these techniques to unlock the full potential that AI technology has to offer.
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