Prompt Engineering

What is Prompt Engineering?

Prompt Engineering = Art of writing prompts to get best results from LLMs.

Bad prompt: "Explain Python"
LLM: [vague, generic response]

Good prompt: "Explain Python to a complete beginner who has never coded before. Use simple language and give 2-3 real-world examples."
LLM: [detailed, beginner-friendly response]

The difference: Better prompt → Better output!

Core Principles

1. Be Specific & Clear

Bad: "Tell me about machine learning"
Good: "Explain the difference between supervised and unsupervised learning. Give a concrete example of each."

2. Give Context

Bad: "Translate this"
Good: "Translate the following Spanish sentence to English: 'El gato está en la mesa.'"

3. Specify Output Format

Bad: "Summarize this article"
Good: "Summarize this article in 2-3 sentences. Format as: Key point 1: ... Key point 2: ..."

4. Use Examples (Few-Shot Prompting)

Without examples:
"Classify sentiment: 'This movie is great!' → ?"

With examples:
"Classify sentiment:
- 'This movie is great!' → Positive
- 'Terrible waste of time' → Negative
- 'Pretty good movie' → ?"

LLM learns from examples!

5. Chain-of-Thought (CoT)

Ask the model to "think through" problems:

Bad prompt: "If there are 5 apples and 3 are red, what percent are red?"

Good prompt: "If there are 5 apples and 3 are red, what percent are red? Think step-by-step."

LLM output:
Step 1: Red apples = 3
Step 2: Total apples = 5
Step 3: Percentage = (3/5) × 100 = 60%
Answer: 60%

Better reasoning, fewer mistakes!

Advanced Techniques

Role-Playing

Prompt: "You are an expert Python teacher. Explain decorators to a student who knows functions."

LLM: [Uses teaching expertise, adjusts explanation level]

Temperature & Randomness

Temperature = 0 (Deterministic)
- Always picks most likely word
- Good for factual questions

Temperature = 1.0 (Balanced)
- Standard randomness
- Good for general use

Temperature = 2.0 (Creative)
- Picks random words
- Good for creative writing

Using System Prompts

System: "You are a friendly Python tutor. Explain concepts simply but accurately."
User: "What is a list?"

LLM: [Acts as friendly tutor, explains simply]