In this section, we will focus on tasks to process inputs, specifically the tasks that take the input and generate a useful output. This is often done through a series of steps. It is important for the model to reason in detail about a problem before answering a specific question. If you have taken our previous course, ‘Chat GBT Prompt Engineering for Developers,’ you will have seen examples of this. Sometimes, it is necessary to ask the model to reason about a problem in steps, which we call the ‘Chain of Thought’ reasoning strategy. This strategy helps prevent errors by rushing to an incorrect conclusion. It allows the model to think longer and more methodically about the problem.
The reasoning process that our model uses to arrive at a final answer may not always be appropriate to share with the user. For example, in tutoring applications, we may want to encourage students to work on their own answers without revealing the model’s reasoning process. To mitigate this, we can use the tactic of ‘inner monologue,’ which involves instructing the model to put hidden parts of the output into a structured format. This way, only the relevant output is made visible to the user.
Let’s consider an example to illustrate this process. Suppose we have a classification problem where we need to classify a customer query into a primary and secondary category. Based on this classification, we may want to provide different instructions. For instance, if the query is about product information, we would include information about the available products. To achieve this, we can use a series of steps to reason through the user’s query and provide a tailored response.
Here is an example of how we can format the instructions for the model:
Step 1: Decide if the user is asking a question about a specific product or products. (Product category doesn’t count.) Step 2: If the user is asking about specific products, identify whether the products are in the list of available products. Step 3: If the message contains products from the list above, list any assumptions the user is making in their message. Step 4: If the user made any assumptions, figure out whether the assumptions are true based on the product information. Step 5: Politely correct any incorrect assumptions and answer the customer’s query in a friendly tone.
By following this format, we can guide the model to reason through the user’s query and provide a relevant response. It is important to note that more advanced language models like GPT-4 can handle complex instructions better. However, it is always a good idea to experiment with different prompts to find the optimal trade-off between complexity and effectiveness.
In conclusion, processing inputs involves reasoning through a series of steps to generate a useful output. By using strategies like the ‘Chain of Thought’ reasoning and ‘inner monologue,’ we can guide the model to provide tailored responses to user queries. In the next video, we will explore another strategy to handle complex tasks by breaking them down into simpler subtasks.
