Beyond Basic Prompts: Advanced Techniques That Actually Work
Once you've mastered basic prompting, you'll hit a plateau. Your prompts work, but results feel generic. The AI understands what you want, but doesn't deliver exceptional output.
That's when you need advanced techniques. Not academic theories, but practical methods that consistently improve results.
1. Chain-of-Thought Prompting
The problem: AI jumps to conclusions without showing its work, often missing nuance or making reasoning errors.
The solution: Force the AI to think step-by-step.
Before
Should we expand to the European market?
Result: Generic yes/no with standard business reasoning.
After
We're considering expanding our SaaS product to Europe. Think through this decision step by step: 1. What data would we need to collect first? 2. What are the 3 biggest risks specific to our product? 3. What would success look like in 12 months? 4. What's the minimum viable expansion plan? Show your reasoning at each step before making a recommendation.
Result: Structured analysis with specific, actionable insights.
When to use: Complex decisions, troubleshooting, strategic planning, anything where reasoning matters more than speed.
2. Few-Shot Prompting
The problem: Describing what you want is harder than showing it.
The solution: Provide examples of the output format and style you want.
Before
Write product descriptions for our new line of ergonomic chairs.
Result: Generic descriptions that don't match your brand voice.
After
Write product descriptions in this exact style: Example 1 (for standing desk): "The Atlas Standing Desk isn't just adjustable—it's adaptive. Engineered for people who think on their feet, with whisper-quiet motors and memory presets for your perfect height. Because great ideas don't always come sitting down." Example 2 (for monitor arm): "Float your screens at the perfect angle. The Horizon Monitor Arm eliminates clutter and neck strain with smooth 360° rotation and tool-free installation. Your workspace, elevated." Now write descriptions for: 1. Ergonomic office chair 2. Keyboard wrist rest 3. Desk lamp with adjustable color temperature
Result: Consistent brand voice and style across all products.
When to use: Maintaining consistency, teaching specific formats, matching existing content style.
3. Role Assignment
The problem: Generic AI gives generic advice.
The solution: Ask the AI to adopt a specific expertise or perspective.
Before
How can we improve our mobile app onboarding?
Result: Standard UX advice you could find anywhere.
After
You are a senior product manager at a top productivity app company. You've specialized in onboarding flows for 10 years and have increased conversion rates by 300% across 5 different apps. Our app helps small businesses manage inventory. Current onboarding has 7 steps and 40% drop-off after step 3. What would you test first? What are the most common mistakes you see in onboarding, and how would you avoid them? What metrics would you track to know if changes are working?
Result: Expert-level, specific advice based on the assigned role's expertise.
When to use: Needing specialized knowledge, getting different perspectives, adjusting tone and depth.
4. Constraint-Based Prompting
The problem: AI defaults to comprehensive, verbose responses.
The solution: Explicitly define what to exclude or limit.
Before
Summarize this research paper.
Result: Long summary that includes everything, including irrelevant details.
After
Summarize this research paper in exactly 3 bullet points. Constraints: - Maximum 15 words per bullet - Focus only on practical applications for software developers - Do not mention the methodology or authors - Use simple language (no academic jargon) Paper: [PASTE PAPER]
Result: Concise, focused summary matching your exact needs.
When to use: Controlling output length, removing unwanted content, enforcing specific formats.
5. Iterative Refinement
The problem: Complex tasks can't be fully specified upfront.
The solution: Treat prompting as a conversation, not a single request.
Round 1: "Give me 10 ideas for a blog post about remote work productivity." Round 2: "I like ideas 3 and 7. Develop full outlines for both with H2s and key points." Round 3: "Let's go with idea 3. Write the introduction and first section." Round 4: "This is too formal. Make it more conversational and add a personal anecdote hook." Round 5: "Better. Now add data points to support the claims in paragraph 3."
When to use: Complex creative work, when you're not sure exactly what you want, polishing and refining.
Putting It All Together
The most powerful prompts combine multiple techniques:
You are an experienced UX researcher (Role Assignment). I'm showing you 3 examples of how I've analyzed user feedback in the past (Few-Shot). Now analyze these 10 new feedback items using the same approach. For each item, provide: - Category - Sentiment score (1-5) - Priority level - Suggested next action Think through ambiguous cases step by step (Chain-of-Thought). Do not include the original feedback text in your output—just the analysis (Constraint).
When to Use Which Technique
| Situation | Best Technique | Example Phrase |
|---|---|---|
| Complex reasoning needed | Chain-of-Thought | "Think step by step" |
| Need consistent format/style | Few-Shot | "Here are examples..." |
| Want expert perspective | Role Assignment | "You are a [expert]..." |
| Output too long/unfocused | Constraint-Based | "Do not include..." |
| Complex creative task | Iterative Refinement | Multi-round conversation |
Practice Exercise
Take a task you do regularly and apply one advanced technique. Notice the difference in output quality.
Example: If you regularly write meeting summaries, try Few-Shot prompting with 2-3 examples of your preferred summary format.
Next Steps
Master these techniques one at a time. Start with Chain-of-Thought (it improves almost everything), then add others as needed.
For ready-to-use examples, check our Prompt Library where we've applied these techniques to common tasks.