Prompt Engineering, Techniques

🔧 Prompting Techniques with Templates

1. Zero-shot Prompting

Use when: The task is simple and doesn’t need examples.

🧪 Template:

Summarize the following email in one sentence: [Insert email text here] 

🧠 Tip: Works best with clear, unambiguous instructions.

2. Few-shot Prompting

Use when: You want to guide the model with examples.

🧪 Template:

Convert the following sentences into passive voice: Example 1: Input: The cat chased the mouse. Output: The mouse was chased by the cat. Example 2: Input: She writes a letter. Output: A letter is written by her. Now convert: Input: They built a house. Output: 

🧠 Tip: Keep examples close in structure to your actual input.

3. Chain-of-Thought (CoT) Prompting

Use when: The task involves reasoning or multiple steps.

🧪 Template:

Question: A bookstore sells books for $12 each. If you buy 3 books and pay with a $50 bill, how much change do you get? Think step by step. 

🧠 Tip: Add “Let’s think step by step” to encourage reasoning.

4. ReAct Prompting (Reasoning + Acting)

Use when: You want the model to reason and then take an action (e.g., search, call a tool).

🧪 Template (for agent-based systems):

Question: What is the capital of the country where the Eiffel Tower is located? Thought: The Eiffel Tower is in Paris, which is in France. So the capital of France is Paris. Action: Return "Paris" 

🧠 Tip: Combine with LangGraph or LangChain agents for tool use.

5. Role Prompting

Use when: You want the model to adopt a specific persona or tone.

🧪 Template:

You are a senior backend engineer mentoring a junior developer. Explain the concept of RESTful APIs in simple terms with examples. 

🧠 Tip: Great for tone control, teaching, or simulations.

6. Self-Consistency Prompting

Use when: You want more reliable answers from multiple generations.

🧪 Template:

Question: If a car travels 60 km in 1.5 hours, what is its average speed? Think step by step. 

🧠 Tip: Run multiple generations and pick the most consistent answer.

7. Instruction + Format-Constrained Prompting

Use when: You need structured output (e.g., JSON, YAML, Markdown).

🧪 Template:

Extract the following email into structured JSON with keys: sender, subject, summary. Email: --- From: John Doe <john@example.com> Subject: Meeting Reminder Body: Just a reminder that we have a meeting tomorrow at 10 AM. --- Output: { "sender": "John Doe", "subject": "Meeting Reminder", "summary": "Reminder about a meeting scheduled for tomorrow at 10 AM." } 

🧠 Tip: Use this when integrating with APIs or downstream systems.

🧠 How to Avoid Hallucinations (with Prompting Tips)