Unlock the full potential of LLMs. Learn to guide, constrain, and optimize AI behavior through precise instructions.
Understanding the different modes of instructing an AI model.
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Asking the model to perform a task without any examples.
Example: "Translate 'Hello' to French."
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Providing 1-3 examples before the actual request to guide style and format.
Significantly improves reliability.
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Asking the model to "think aloud" or break down steps before answering.
Crucial for math and logic puzzles.
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The initial instruction that defines behavior.
"You are a helpful coding assistant who explains bugs clearly."
See how different prompting styles lead to different outcomes. Click the buttons to simulate the model's path.
Standard prompts often rush to an answer. Chain of Thought forces the model to show its work, reducing errors.
Without specific instructions, LLMs predict the most likely *next tokens* immediately. For simple math, this often works, but for complex logic, it can lead to "hallucinated" shortcuts.
By adding "Let's think step by step", you force the model to generate intermediate tokens. These tokens act as a "scratchpad," allowing the model to ground its final answer in its own previous reasoning.
Models are chatty. Constraints rein them in.
"Do NOT apologize. Do NOT say 'As an AI language model'. Do NOT include fluff."
"Answer in exactly 3 sentences." or "Summarize in under 50 words."
Test your Prompt Engineering skills.
1. What is "Few-Shot" prompting?
2. Why is "Chain of Thought" useful?
3. What is the best way to get a JSON output?