Understand how large language models work, from tokenization to context windows to hallucination.
LLMs predict text, they don't understand it. Here's how large language models work under the hood, from training to transformers to next-token prediction, and why it matters for how you use them.
Temperature controls how random or deterministic an AI model's output is. Here's what it does technically, how it relates to top-p and top-k, and when to adjust it.
Context windows determine how much an AI model can 'see' at once. Here's what they are technically, how attention scales, and practical strategies for working within their limits.
Tokenization isn't just a technical detail. It shapes how LLMs process your input. Understanding it changes the way you write prompts.
AI hallucination isn't a bug you can patch. It's a consequence of how language models work. Here's what causes it, how to measure it, and what actually reduces it.
Streaming LLM responses reduces perceived latency and improves UX. Here's how server-sent events work, how to implement streaming with OpenAI and Anthropic, and what to watch for in production.
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Master system prompts, few-shot techniques, chain of thought reasoning, and structured output.
Get Insanely Good at AI
Chapter 2: How AI Actually Worksgoes deeper into the mechanics: tokenization, transformers, and next-token prediction. The understanding that changes how you work with every AI tool.
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