Level 1: Basic Prompting - Transactional Exchanges

When we first encounter AI systems, our mental model is strikingly simple: AI is a question-answering machine, a tool at our disposal. We approach it with the same mindset we might use with a search engine or calculator—expecting direct, immediate responses to our queries.

At this level, success feels binary—either you got what you wanted, or you didn't. When the response misses the mark, the natural reaction is often frustration: "Why didn't it understand what I meant?"

Parallel Examples from Different Domains

Attention Mechanisms & The Semantic Vector Space at Level 1

At the basic prompting level, you're navigating the default probability landscape of the AI's training. All the nuance that may emerge is the training data + a random element (called sampling).

Your simple queries activate standard response patterns without reshaping how the AI processes information. You can ask the same question in 10 chats and you'll get different but similar answers, which should make you start wondering, Why?

def sort_list(my_list):
    return sorted(my_list)

The Aha Moment: Basic prompting's breakthrough moment comes when you first successfully retrieve exactly what you need. "Wow, it actually gave me useful information!" This early success establishes your foundational relationship with AI systems. This is when you might start wondering and seeing why you got what you wanted.

Looking Ahead: Moving Beyond Basic Prompting

While Basic Prompting is incredibly useful for straightforward questions and simple tasks, you'll eventually notice its limitations. Sometimes the AI gives you exactly what you want, and other times it completely misses the mark.

In Level 2: Structured Prompting, we'll explore how the way you phrase your prompts can dramatically improve the quality and relevance of AI responses.

Happy Prompting!

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