Understanding LLM Architecture

Large language models power most of today's AI applications. Understanding their architecture helps you use them effectively and avoid common pitfalls.

From tokens to text

LLMs take a sequence of tokens (subword units) as input and predict the next token repeatedly. The core is the transformer block: self-attention and feed-forward layers that allow the model to use context from the whole sequence.

Why context length matters

• Context window is the maximum number of tokens the model can process at once.
• Longer context enables more documents in RAG, longer conversations, and fewer truncation hacks—but it increases cost and latency.
• Choose context length based on your use case; don't assume bigger is always better.

Implications for integration

1. Prompt design — Structure your prompt so the most important information is where the model "looks" (often the start and end of the context).
2. Streaming — Models output token-by-token; use streaming for better perceived latency.
3. Caching — Repeated prefixes (e.g., system prompt + docs) can be cached to save compute.

Understanding these basics will help you design better prompts, choose the right model size, and debug production issues.