Understanding Tokens, Context Windows, and Memory Limitations in LLMs
This article explains key concepts about how large language models (LLMs) work, including tokens, context windows, and the lack of memory between calls. It provides practical guidance for developers building on top of LLMs.
Why it matters
Knowing the technical details of how LLMs work, including tokens, context windows, and memory limitations, is essential for developers to build reliable and robust AI-powered applications.
Key Points
- 1Tokens are not the same as words - LLMs process tokenized text, not natural language
- 2Tokenization can result in longer representations for non-English text and code compared to English prose
- 3Context windows are a fixed-size budget that limits how much text the model can process at once
- 4LLMs have no memory between calls, so they cannot 'remember' previous messages or code
Details
The article explains that LLMs do not actually read English words, but rather process 'tokens' - chunks of text identified by the model's tokenizer algorithm. This can lead to surprising differences in token counts between languages and between prose and code. For example, a short Japanese sentence may use as many tokens as a full line of Python code. The article also discusses the concept of a 'context window' - the fixed-size budget of tokens that the model can process in a single call. Current LLM tiers range from 8,000 tokens up to 2 million tokens. Finally, the article emphasizes that LLMs have no memory between calls, so they cannot 'remember' previous messages or code that was provided. Understanding these fundamental concepts is crucial for developers building applications on top of LLMs.
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