Posts tagged “llm”

AI systems stream responses with variable length and timing. Here's how to design interfaces that show progress immediately and handle uncertainty gracefully.

How to test LLM outputs with code-based grading, human evaluation, and LLM-as-judge. When to use each method and why statistical rigor matters.

Understanding what fills an LLM's context window and how it affects model behavior.

Definition and explanation of tokens in large language models.

Five prompting techniques that improve LLM outputs: few-shot learning, chain-of-thought reasoning, XML structure, output constraints, and prompt chaining.

Your prompt's opening sets the context for the entire response.

LLMs generate text one token at a time. Understanding how they convert text to vectors, use attention to weigh context, and predict probabilities explains their behavior.

When models fail or behave unexpectedly, you need to understand why. Practical debugging techniques for tokenization, attention patterns, and context limits.