Divide the text into smaller, logical sections
Posted: Sat Apr 05, 2025 6:53 am
Chunking: Efficient processing of large amounts of text
When large amounts of text need to be processed by an AI, chunking can be a useful technique. This involves breaking the text into smaller paragraphs or sentences and sending each one separately to the AI. This optimizes token usage and saves storage space.
A common mistake is sending the entire text in a single request , as this can exceed the model's maximum token limit. Equally problematic is chinese overseas africa database converting the entire text to a simple string and using a high temperature setting to obtain detailed responses—this will result in inconsistent or disjointed responses.
The recommended procedure is:
Send each section separately to the AI and save the answers .
Combine the partial answers into a coherent text .
This not only improves accuracy but also the consistency of responses.
Temperature values and their impact on AI models
The temperature setting of an AI model determines how creative or deterministic the answers are.
Temperature = 0 ensures consistent and predictable answers because the model always chooses the most likely answer.
Temperature = 1 allows more variance and creativity in the answers.
Temperature = 1.5 leads to even more free answers, which may lead to unexpected or illogical results.
Temperature = 2 would not improve the evidence but would lead the model to extremely random answers.
When large amounts of text need to be processed by an AI, chunking can be a useful technique. This involves breaking the text into smaller paragraphs or sentences and sending each one separately to the AI. This optimizes token usage and saves storage space.
A common mistake is sending the entire text in a single request , as this can exceed the model's maximum token limit. Equally problematic is chinese overseas africa database converting the entire text to a simple string and using a high temperature setting to obtain detailed responses—this will result in inconsistent or disjointed responses.
The recommended procedure is:
Send each section separately to the AI and save the answers .
Combine the partial answers into a coherent text .
This not only improves accuracy but also the consistency of responses.
Temperature values and their impact on AI models
The temperature setting of an AI model determines how creative or deterministic the answers are.
Temperature = 0 ensures consistent and predictable answers because the model always chooses the most likely answer.
Temperature = 1 allows more variance and creativity in the answers.
Temperature = 1.5 leads to even more free answers, which may lead to unexpected or illogical results.
Temperature = 2 would not improve the evidence but would lead the model to extremely random answers.