Imagine you're tasked with editing a lengthy report. You don't start by reading every single word from start to finish or trying to memorize the entire document. Instead, your approach is intuitive and efficient:
Scan for Relevant Sections: You quickly skim through the headings and subheadings to locate the areas that need attention.
Jump Directly to Where You Need to Be: Without getting bogged down by unrelated content, you navigate straight to the specific section that requires editing.
Make Your Targeted Changes: Focus solely on modifying the necessary parts, ensuring precision and maintaining the document's overall integrity.
Move On to the Next Task: Once your edits are complete, you proceed to other tasks without unnecessary delays.
This natural editing process enhances productivity, reduces errors, and ensures that your attention is directed where it's most needed. However, when leveraging AI for document editing, we often overlook this human-centric approach. Instead, we default to having AI process entire documents to make even minor changes - a method that's inefficient and counterintuitive.
While humans excel at focusing on relevant sections and making precise edits, Large Language Models (LLMs) like GPT-4 tend to process entire documents in a brute-force manner. This approach not only escalates operational costs and processing time but also heightens the risk of introducing errors due to the sheer volume of content being handled.
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In this blog post, I will discuss an innovative method called Selective Processing Editor (SPE) that uses function calls to imitate human behavior, making the process more efficient, cost-effective, and reliable.
Previously, we discussed a feature called Predicted Outputs, which aims to reduce latency by up to 30% by anticipating portions of the response that remain unchanged. While Predicted Outputs offer faster responses, they still require reading and passing the entire document to OpenAI for processing, which can lead to higher processing costs and increased resource usage.
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