Advanced Techniques
Once you've mastered the basics of prompt engineering, these advanced techniques will help you get even better results for complex tasks.
1. Structured Thinking ("Step-by-Step")
Ask the model to reason through problems in stages rather than jumping straight to conclusions.
How It Works
Break down complex tasks into logical steps and explicitly request the AI to follow this structure.
Example
Analyze this business proposal and work through it step-by-step:
Step 1: First, list all the key assumptions in the proposal
Step 2: Then, analyze the financial data and projections
Step 3: Next, evaluate the risk factors
Step 4: Finally, provide your overall conclusion and recommendations
For each step, be explicit about your reasoning before moving to the next step.
When to Use
- Complex analysis requiring logical progression
- Financial modeling and forecasting
- Strategic decision-making
- Multi-faceted problem-solving
2. Few-Shot Examples
Provide 1-2 examples of the exact style or format you want, then ask the AI to replicate it with new content.
How It Works
Show the AI what "good" looks like by providing concrete examples, then request the same format for your specific case.
Example
I need product descriptions in a specific format. Here are two examples:
Example 1:
Product: Swiss Mountain Coffee
Hook: Wake up to Alpine excellence
Benefits: Sustainably sourced from Swiss highlands | Rich, smooth flavor | Fair-trade certified
CTA: Order your first bag today
Example 2:
Product: Precision Timepiece Watch
Hook: Swiss engineering meets timeless design
Benefits: 50-year warranty | Handcrafted in Zürich | Water-resistant to 100m
CTA: Discover the collection
Now produce the same format for this new product:
Product: Organic Chocolate Bar
[Include relevant details about sourcing from Swiss Alps, 85% cocoa, etc.]
When to Use
- Maintaining consistent brand voice across content
- Creating standardized reports or documents
- Generating multiple items with the same structure
- Teaching the AI your preferred style
3. Role-Playing for Expertise
Assign the AI a highly specific expert role to tap into specialized knowledge and perspective.
How It Works
Define a detailed expert persona that matches the depth and specialization you need.
Example
You are a Swiss data privacy lawyer with 15 years of experience in FADP and GDPR compliance for financial institutions.
Review this data processing clause and:
1. Highlight any potential FADP/GDPR compliance risks
2. Identify missing elements required for Swiss legal compliance
3. Suggest specific improvements with legal justification
4. Rate the current clause on a scale of 1-10 for compliance
Use Swiss legal terminology and reference specific FADP articles where applicable.
When to Use
- Legal and regulatory reviews
- Technical architecture decisions
- Industry-specific analysis
- Compliance and risk assessment
Tips for Effective Role-Playing
- Be specific about years of experience and specialization
- Include relevant industry context
- Mention specific frameworks, standards, or regulations
- Define the expected level of technical depth
4. Iterative Refinement
Start with a broad request, then progressively narrow the focus based on initial results.
How It Works
Use a multi-turn conversation to drill down into specifics, rather than trying to get everything perfect in one prompt.
Example Sequence
Turn 1 (Broad):
Analyze this quarterly financial report and identify the main areas of concern.
Turn 2 (Narrower):
Now focus specifically on the financial risks you identified. Which ones pose the greatest threat to our liquidity?
Turn 3 (Very Specific):
For the top liquidity risk, quantify the potential impact if our largest client reduces orders by 30%, and suggest 3 mitigation strategies.
When to Use
- Exploratory analysis where you're not sure what you'll find
- Complex reports requiring multiple perspectives
- When the scope is too large for one prompt
- Collaborative problem-solving sessions
5. Chain-of-Thought Reasoning
Explicitly request the AI to show its reasoning process, not just the final answer.
How It Works
Ask the AI to "think out loud" and explain each logical step.
Example
Before providing your final recommendation, walk me through your complete reasoning process:
1. What are the key factors to consider?
2. What are the pros and cons of each option?
3. What assumptions are you making?
4. How did you weigh different factors?
5. What's your final recommendation and why?
Show all your work - I want to understand how you arrived at the conclusion.
When to Use
- High-stakes decisions requiring transparent reasoning
- Educational purposes where understanding the process matters
- Quality control and verification
- Building trust in AI-generated recommendations
6. Constraints and Negative Prompting
Tell the AI what NOT to do, in addition to what TO do.
How It Works
Explicitly exclude unwanted approaches, topics, or formats.
Example
Create a marketing email for our wealth management services.
Do include:
- Emphasis on Swiss data privacy and security
- Our 50-year track record
- Personal service approach
Do NOT include:
- Promises of guaranteed returns
- Aggressive sales language
- Comparisons to competitors
- Generic investment advice
Tone: Professional, trustworthy, understated elegance
When to Use
- Regulated industries with compliance requirements
- Brand voice management
- Avoiding common AI pitfalls
- Sensitive topics requiring careful handling
Combining Techniques
The most powerful prompts often combine multiple advanced techniques:
You are a Swiss financial risk analyst with expertise in ESG investing. [Role-playing]
Analyze this portfolio step-by-step: [Structured thinking]
1. First, categorize each holding by ESG score
2. Then, identify concentration risks
3. Next, compare to the benchmark
4. Finally, provide recommendations
Here's an example of the format I want for your findings: [Few-shot]
[Include example format]
Focus only on material ESG risks that could impact returns. [Constraints]
Avoid discussing technical ESG scoring methodology. [Negative prompting]
Show your reasoning for each recommendation. [Chain-of-thought]
Practice Makes Perfect
These advanced techniques become more natural with practice. Start by:
- Experimenting with one technique at a time
- Comparing results with and without the technique
- Documenting what works best for your common tasks
- Iterating to find your optimal approach
Master these techniques and you'll unlock the full potential of AI for your most complex and valuable work.