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LLMs Usage FAQ: Difference between revisions
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→How to Make AI Process Long Articles
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💬 Processing Methods: | 💬 Processing Methods: | ||
Method 1: Switch to | Method 1: Switch to models that support longer context windows, such as Google Gemini: | ||
Method 2: Chunking | # GPT-4o: "16,384 max output tokens"<ref>[https://platform.openai.com/docs/models/gpt-4o Model - OpenAI API]</ref> equivalent to approximately 5,461 Chinese characters (16,384/3) | ||
# gemini-2.5-pro: "65,536 max output tokens"<ref>[https://ai.google.dev/gemini-api/docs/models#gemini-2.5-pro Gemini 2.5 Pro]</ref> equivalent to approximately 21,845 Chinese characters (65,536/3) | |||
# GPT-5: "128,000 max output tokens"<ref>[https://platform.openai.com/docs/models/gpt-5 Model - OpenAI API]</ref> equivalent to approximately 42,666 Chinese characters (128,000/3) | |||
Method 2: Start a new conversation and transfer the conversation content to the new dialogue. For existing conversations, you can try using this prompt: | |||
<pre> | |||
As the first prompt for a new conversation, please organize our previous dialogue into: | |||
1. Clear operational steps | |||
2. Instructions to verify the success of each prerequisite step | |||
</pre> | |||
Method 3: Chunking strategy with context continuity maintenance | |||
When processing long texts, we need to adopt chunking technical strategies<ref>[https://ihower.tw/blog/archives/12373 使用繁體中文評測 RAG 的 Chunking 切塊策略 – ihower { blogging }]</ref>. To help the model understand the context of previous chapters when processing subsequent paragraphs, an effective approach is '''Chunking Strategy with Previous Article Summarization''': | When processing long texts, we need to adopt chunking technical strategies<ref>[https://ihower.tw/blog/archives/12373 使用繁體中文評測 RAG 的 Chunking 切塊策略 – ihower { blogging }]</ref>. To help the model understand the context of previous chapters when processing subsequent paragraphs, an effective approach is '''Chunking Strategy with Previous Article Summarization''': | ||