LLMs Usage FAQ

Common Questions and Answers about Using LLMs

🌐 Switch language: EN, 漢字

Force Traditional Chinese Output

• Add #zh-TW before your question
• Or say "Use Traditional Chinese commonly used in Taiwan"

```
Use Traditional Chinese commonly used in Taiwan:

Rules
- Use full-width punctuation marks and add spaces between Chinese and English text.
- Below is a common AI terminology correspondence table (English -> Traditional Chinese):
  * Transformer -> Transformer
  * Token -> Token
  * LLM/Large Language Model -> 大語言模型
  * Zero-shot -> 零樣本
  * Few-shot -> 少樣本
  * AI Agent -> AI 代理
  * AGI -> 通用人工智慧
- The following is a table of common Taiwanese terms (English -> Traditional Chinese):
  * create -> 建立
  * quality -> 質量
  * information = 資訊
  * message = 訊息
  * store = 儲存
  * search = 搜尋
  * view = 檢視, 檢視表 (No 視圖 as always)
  * create = created = 建立
  * data = 資料
  * object = 物件
  * queue = 佇列
  * stack = 堆疊
  * invocation = 呼叫
  * code = 程式碼
  * running = 執行
  * library = 函式庫
  * building = 建構
  * package = 套件
  * video = 影片
  * class = 類別
  * component = 元件
  * Transaction = 交易
  * Code Generation = 程式碼產生器
  * Scalability = 延展性
  * Metadata =  Metadata
  * Clone = 複製
  * Memory = 記憶體
  * Built-in = 內建
  * Global = 全域
  * Compatibility = 相容性
  * Function = 函式
  * document = 文件
  * example = 範例
  * blog = 部落格
  * realtime = 即時
  * document = 文件
  * integration = 整合
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Generating Longer Article Content

📝 Problem: I want to use LLMs to generate articles of 5000-6000 words, but each attempt only produces articles of 1000-1500 words.

💬 Reason: LLM models have context window length limitations, with a fixed limit on the total number of tokens for input and output combined in each request. Therefore, each generation result encounters an upper limit of 1000-1500 words. The recommended workaround is to break down the intended article structure and generate content chapter by chapter.

Using OpenAI o3 model as an example: (1) Context Window (200,000): total quota for input + output, (2) Max Output Tokens (100,000): single response limit. Actual input space: 200,000 - expected output length

Solution: If it's not possible to generate a 5000-6000 word article in one go, you can pre-plan a five-chapter structure in your input instructions, then generate content sequentially according to the chapter order, ultimately achieving the goal of producing a 5000-6000 word article.

How to Solve AI Forgetting Training Content

📝 Inquiry:

I'd like to ask a follow-up question: If we adopt a "layer-by-layer prompt optimization" approach to improve AI performance, might we encounter the following situation: After multiple rounds of prompt optimization, the AI does learn the relevant skills and performs well, but after some time, it forgets these trained capabilities?

I want to understand whether current mainstream AI model platforms all have stable memory retention capabilities - that is, can they continuously remember the training prompts and guidance we've previously provided?

Sometimes I feel that AI's memory seems unstable. During the same project, content and requirements that I've already explained to the AI in detail need to be re-explained from scratch after a while, which makes me question the continuity of AI learning.

💬 Response:

Indeed, early AI models, due to shorter context window limitations, were prone to drifting from their original settings. When I encounter such situations, I usually choose to start a completely new conversation and restart the entire interaction process.

Current AI models should have significant improvements in this regard. If this conversation's results are satisfactory, I suggest you can give the AI an instruction to summarize and consolidate the entire conversation process, integrating the accumulated interaction principles and experiences from the dialogue into the initial prompt:

Assuming I want to start a new conversation to discuss the same topic, please suggest what complete prompt I should use.

This prompt needs to include important content from our entire discussion process:
(1) The core problems and objectives that the original prompt aimed to solve
(2) Important aspects and details related to the original problem that the initial solution method didn't fully consider