Imagine editing a lengthy report without reading every word. You'd scan for relevant sections, jump to where you need to be, make targeted changes, and move on. This natural editing process enhances productivity and reduces errors. However, when using AI for document editing, we often overlook this human-centric approach.
Large Language Models (LLMs) like GPT-4 tend to process entire documents in a brute-force manner, which escalates costs, increases processing time, and heightens the risk of errors. This blog post introduces 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.
The problem with current AI document editing:
- Cost: Feeding the entire document means paying for every token.
- Speed: Processing everything slows down the operation.
- Reliability: More content increases the chance of errors.
- Context Limits: Large documents may exceed the model's input limits.
- Purpose Misalignment: LLMs are built to generate text, not to navigate and edit existing documents efficiently.
SPE adopts a strategy inspired by human editing practices, combining fast, deterministic tools with AI's intelligent capabilities. This hybrid method mirrors the natural workflow of human editors, allowing for targeted modifications without processing entire documents.
Tools (Fast, Free, Reliable):
- Find sections instantly using grep
- Extract exact lines in milliseconds using sed
- Perfect merging using cat
AI (Smart but Used Sparingly):
- Only see section map
- Pick the right section
- Edit just that piece
Continue reading the full article on Medium to learn more about the SPE approach, its implementation details, real-world performance examples, and potential applications.
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