How LLMs Think
Large Language Models (LLMs) are prediction engines. They don't "know" things; they predict the next most likely word based on the context you provide. To get the best results, you need to structure your request clearly.
The Core Components
[Instruction] + [Context] + [Input Data] + [Output Indicator]
1. Instruction
Tell the model exactly what to do. Use active verbs.
- ❌ "Can you maybe write a poem?"
- ✅ "Write a haiku about coding."
- ✅ "Summarize," "Translate," "Classify," "Generate."
2. Context
Give the model background information to narrow down the possibilities.
"I am a beginner in Python. Explain loops to me using a cooking analogy."
3. Input Data
The text you want the model to process.
4. Output Indicator
Tell the model how you want the answer formatted.
- "Format the answer as a bulleted list."
- "Return the result in JSON format."
- "Limit the response to 50 words."
🔑 Key Takeaway: The clearer your prompt, the better your results. Always include what you want, the context, and the format.