base-seoul

#dailychallenge

How LLMs understand human(NLP) and
how to make it understand better for non-engineers.

Large Language Models (LLMs) are trained on vast datasets, learning statistical patterns in text rather than true understanding.

Modern LLMs use self-attention mechanisms to focus on the most relevant parts of the input.

Since LLMs predict text rather than comprehend it, understanding their training data helps improve their outputs.

You can explore research papers or documentation to identify the datasets used for training.

For non-experts, this might be challenging, so alternative approaches can be useful.

A simple yet effective method is using markdown formatting for structured inputs.

Since LLMs are trained on developer-friendly documents, they process markdown more efficiently.

Markdown provides consistency, making it easier for LLMs to follow patterns.

Well-structured prompts improve response accuracy.

You can also apply Chain-of-Thought (CoT) reasoning manually to enhance logical output.