By the end of this module, you will understand how Large Language Models work, be able to deconstruct prompts into their fundamental components, and effectively control AI responses through temperature and model settings.
You will learn:- The statistical mathematics behind LLMs
- Prompt anatomy and component identification
- Comparative analysis of different AI models
- Temperature and parameter effects on AI responses
- Best practices for clear prompt structure
What is an LLM? Not Magic, but Statistical Mathematics
A Large Language Model (LLM) is a sophisticated statistical predictor based on the Transformer architecture. Given a sequence of tokens (the context), it calculates the probability distribution of the most plausible next token through self-attention mechanisms.
Test the same prompt on different models:
Prompt: "Explain the flat-rate tax system in two lines for a digital freelancer."Task:
- Execute the prompt on at least two different models (e.g., ChatGPT, Claude, Gemini)
- Record the responses and compare them
- Identify at least 3 substantial differences between the explanations
- Explain why you think these differences exist
Typical observations:
- GPT-4 tends to be more structured and formal
- Claude often provides practical examples
- Gemini may be more concise but less detailed
Why they differ: Each model has different training data, specific optimizations, and "personalities" imposed by reinforcement learning.
Test scenarios:
- Create a prompt to generate a short fantasy story (max 100 words)
- Execute the same prompt with different temperatures: 0.2, 0.7, 1.0
- Document how creativity and coherence change
- Test the same prompt with top_p: 0.5 and 0.9
- Compare results and identify the best setup for creative but coherent stories
Expected results:
- Temperature 0.2: Very similar stories, little variety
- Temperature 0.7: Good balance between creativity and coherence
- Temperature 1.0: Very creative stories but sometimes incoherent
- Top_p 0.5: More focused and predictable responses
- Top_p 0.9: Greater variety in responses
Recommended setup: temperature=0.7, top_p=0.8 for creative but coherent stories.
Task: Decompose this prompt into its 4 fundamental components:
As a digital marketing expert, analyze this landing page for a fitness app.
Identify 3 strengths and 3 areas for improvement.
Provide concrete recommendations to increase conversions.
Landing page: [Landing page text here]
Response format:
1. Strengths
2. Areas for improvement
3. Recommendations
4. Implementation priority (high/medium/low)Identify: Instruction, Context, Input Data, Output Indicator.
- Instruction: "analyze this landing page", "identify 3 strengths and 3 areas for improvement", "provide recommendations"
- Context: "As a digital marketing expert", "for a fitness app", "to increase conversions"
- Input Data: "Landing page: [Landing page text here]"
- Output Indicator: "Response format:" followed by the specified structure