I’m looking for ways to enhance our approval process by providing approvers with better context. Currently, they receive a lot of detailed information, which can be overwhelming and slow down the decision-making process. I’ve been considering integrating AI summarization tools, like Latenode’s access to LLMs, to auto-summarize the requests and flag potential risks. This way, approvers get a concise overview and can focus on making informed decisions quickly. Has anyone else implemented something similar? How did you configure the summarization step to ensure it captures the most important details?
I’ve found that using AI to summarize complex requests not only speeds up the approval process but also ensures that all critical information is highlighted. What works well is defining clear guidelines for what the summary should cover—like key points, deadlines, and potential issues. This way, the AI can focus on extracting the most relevant information and present it in a digestible format.
In my experience, integrating LLMs into workflows can be incredibly powerful for summarization tasks. One approach that has worked well is to train the model with a set of example summaries that illustrate the desired output. This ensures that the AI learns to extract the most critical information and present it in a format that’s easy for approvers to consume.
train ai models on past summaries to improve relevance and accuracy.