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Instruction-based prompts

Video Module 4: Prompt structures & patterns

Instruction-based prompts

The Anatomy of a Great Prompt:- To get the best out of an AI, a high-quality instruction-based prompt usually contains four key pillars:

Component

Purpose

Example

Persona

Sets the tone and expertise level.

"Act as a senior data analyst..."

Task

The specific action to be taken.

"...summarize this spreadsheet into three trends."

Context

Background info or constraints.

"The audience is a board of directors who value brevity."

Format

How the output should look.

"Use a bulleted list and a concluding table."

Common Techniques

  1. Few-Shot Prompting:- Instead of just asking for a result, you provide a few examples of what a "good" answer looks like.
    • Prompt: "Classify the sentiment. Example: 'I love this!' -> Positive. 'It's okay.' -> Neutral. 'I hate this.' -> Negative. 'The service was slow.' ->"
    • Result: The AI follows the pattern precisely.
  2. Chain-of-Thought (CoT):- You ask the model to "think step-by-step." This is crucial for complex logic or math.
    • Instruction: "Solve for $x$: $3x + 5 = 20$. Show your work step-by-step."
    • Result: The AI breaks down the logic, reducing the chance of a "hallucinated" or incorrect final number.
  3. Constraint-Based Prompting:- Setting boundaries to prevent "rambling" or off-topic content.
    • Instruction: "Explain quantum entanglement without using the word 'spooky' and keep it under 50 words."
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