Chapter 4: Advanced Techniques
At this stage, you know how to craft clear prompts and apply best practices. Now let’s level up and explore advanced techniques that can help you build powerful workflows and get consistently high-quality output.
Chain-of-Thought + Role Prompts
You can combine multiple techniques — for example, assign a role and ask the model to think step by step. This is especially useful for decision-making and complex reasoning.
Example:
Multi-Prompt Chaining
Instead of one big prompt, break tasks into smaller prompts and feed the output of one step into the next. This allows for iterative refinement and reduces hallucination risk.
Use of Delimiters
Clearly separate sections of your prompt using triple quotes (“””), tags, or markers. This helps the model parse your input cleanly.
Example:
Topic: Customer Retention
Tone: Professional but friendly
Output Format: 3 bullet points
“””
Generate tips based on the above details.
Prompt Parameterization
Create prompt templates with placeholders for dynamic values (e.g., {topic}, {tone}, {format}). This is extremely helpful for automation and API-based workflows.
💡 Tip
🧩 Quick Quiz
1. True or False: Prompt chaining means putting everything into one giant prompt.
Show Answer
❌ False — prompt chaining means breaking down the task into smaller sequential prompts.
2. Multiple Choice: Which technique reduces hallucinations and improves accuracy?
- A. Adding more adjectives
- B. Multi-Prompt Chaining
- C. Making the prompt longer
- D. Ignoring delimiters
Show Answer
✅ B. Multi-Prompt Chaining.
3. Reflection: Identify a process in your work where you could break a single large AI request into two or three steps for better results.