Describe a specific situation where prompt chaining can overcome limitations in a single GPT API call?

Prompt chaining, where the output of one GPT API call is used as the input for a subsequent API call, can overcome limitations in a single API call when dealing with tasks that require exceeding the maximum token limit or performing complex reasoning involving multiple steps. A specific example is summarizing a very long document that exceeds the maximum input token limit for a single API call. Instead of trying to summarize the entire document in one go, which is impossible due to the token limit, prompt chaining can be used to break down the task into smaller, manageable steps. First, the document is split into smaller sections, each within the token limit. Then, each section is summarized individually using a separate API call. Finally, the summaries of each section are combined and fed into another API call to generate a final, comprehensive summary of the entire document. This approach allows the model to process information incrementally and overcome the token limit restriction. Another example is performing complex reasoning or problem-solving tasks that require multiple steps of inference. Suppose you want to extract key insights from a customer review and then use those insights to generate personalized product recommendations. The first API call could be used to analyze the customer review and extract key entities, sentiments, and preferences. The output of this call, containing the extracted insights, is then used as the input for a second API call that generates personalized product recommendations based on those insights. This multi-step approach allows the model to break down the complex task into smaller, more manageable steps, improving the accuracy and relevance of the final output. Without prompt chaining, these tasks would be difficult or impossible to achieve with a single API call.