Self-Consistency: The Prompting Technique That Supercharges Your AI's Intelligence

Discover how to get deeper, more reliable insights by making AI think from multiple angles—just like a team of experts

Your trusted AI considering a response from different angles - Midjourney

Ever gotten an AI response that sounds perfectly reasonable, but left you wondering if you can really trust it? Or asked the same question multiple times only to receive completely different advice each time?

Well, you're not alone! That's exactly why I'm excited to share Self-Consistency with you today—a powerful prompting technique that solves this problem by making AI analyze situations from multiple perspectives. It's like having an entire team of experts tackling your question instead of just one advisor.

What is Self-Consistency and why should you care?

Think about tackling a tough problem at work. Instead of asking just one expert, you bring in a whole team: a finance person looks at the numbers, a strategy person maps out where things might go long-term, and someone from operations figures out how to make it all happen. In the end, you get a much better solution than if you'd only talked to one person.

Self-Consistency does the same thing with AI. Instead of taking a single answer at face value, you get the AI to look at your question from multiple angles—like having your own team of advisors all working for you. The cool part? These different "viewpoints" all come from the same AI but give you unique perspectives on your problem.

Why is this so much more powerful than a standard response?

1. Catches contradictions instantly: When different approaches give you conflicting answers, you know right away you need to look deeper—no more false confidence.

2. Builds genuine trust: When several viewpoints all point to the same conclusion, you can move forward feeling much more confident.

3. Uncovers hidden insights: Each new angle shows you things you might completely miss with just one perspective.

4. Cuts through biases: By mixing different approaches, you avoid getting led astray by any one skewed viewpoint.

How does it really work?

Self-Consistency isn't just asking the same question over and over. The magic happens when you structure your prompt to make the AI look at your problem from completely different angles.

Think of it like chatting with a team where everyone brings a unique specialty to the table. But instead of scheduling five separate meetings, you get all their insights at once, neatly organized and ready to compare.

Let's see it in action

To show you how powerful this can be, I gave this prompt to Claude based on a decision many advertising agencies are facing right now:

Prompt:

As the Director of an advertising agency, you face this decision:

Should you invest significantly in AI creative tools this year or wait for the technology to mature?

Analyze this decision from 5 different perspectives, reasoning step by step in each one. At the end, compare the conclusions from each analysis to reach a final recommendation.

Check out this comprehensive response - notice how each perspective brings something unique to the analysis:

Response:

I'll analyze this strategic decision from 5 different perspectives, considering each one in detail.

1. Financial/ROI Perspective:
- First step: Evaluate initial costs vs potential returns
- Second step: Current AI creative tools have relatively accessible subscription costs (between $30-500/month per user)
- Third step: Potential time savings for creative staff could be 20-30% on routine tasks
- Partial conclusion: Although the initial investment is moderate, the potential ROI is attractive even with current technology

2. Competitive Perspective:
- First step: Analyze what competitors are doing
- Second step: Leading agencies are already implementing AI in their workflows
- Third step: Waiting could mean falling behind technologically
- Partial conclusion: Early adoption could provide a significant competitive advantage, while waiting involves competitive risks

3. Human Talent Perspective:
- First step: Consider the impact on the current creative team
- Second step: Evaluate the learning curve and potential resistance
- Third step: Analyze how AI could free up time for more strategic work
- Partial conclusion: Implementation requires careful change management, but could increase job satisfaction by eliminating repetitive tasks

4. Client Perspective:
- First step: Evaluate client perception of AI use
- Second step: Consider how it will affect service quality and speed
- Third step: Analyze the added value for the client
- Partial conclusion: Clients value innovation and efficiency, but need guarantees regarding quality and originality

5. Technological Perspective:
- First step: Evaluate the current maturity of AI creative tools
- Second step: Consider the speed of technology evolution
- Third step: Analyze integration with existing systems
- Partial conclusion: Although technology evolves rapidly, current tools are already mature enough for professional use

Comparison and Final Recommendation:

Analyzing the conclusions from each perspective, a clear pattern emerges:
- The financial and competitive perspectives strongly suggest early adoption
- The human talent perspective indicates it's manageable with proper planning
- The client perspective shows positive opportunities with manageable risks
- The technological perspective suggests current tools are already viable

Recommendation: Invest in AI creative tools this year, but with a strategic and gradual approach:
1. Start with a pilot project in a specific team
2. Establish clear success metrics
3. Develop a comprehensive training plan
4. Clearly communicate the benefits to clients and employees
5. Maintain a flexible budget to adapt to new tools as technology evolves

Why is this response so powerful?

What makes this response truly valuable is how each perspective brings something completely different to the table:

1. Financial/ROI Perspective

• Cuts through the hype with hard numbers about costs and benefits

• Looks at the decision through the cold, practical lens of economics

• Gives you specific yardsticks to measure whether your investment pays off

2. Competitive Perspective

• Zooms out to show you what's happening across your industry

• Spots strategic dangers and opportunities you might miss otherwise

• Helps you stay one step ahead of what competitors might do next

3. Human Talent Perspective

• Gets real about how changes will affect your actual team

• Flags where you'll need training and support

• Helps you spot and address resistance before it becomes a problem

4. Client Perspective

• Shows you how your decisions look from the client's side of the table

• Anticipates what might worry them about your new approach

• Uncovers ways to turn changes into client benefits

5. Technological Perspective

• Gives you a reality check on what today's tools can actually deliver

• Helps you gauge how quickly things are changing

• Tackles practical implementation challenges head-on

The magic happens when you see how these perspectives work together. Something the financial view considers a risk might be seen as an opportunity from the competitive angle. And when several perspectives independently point to the same conclusion (like starting with a gradual rollout), you can move forward with much more confidence that you're on the right track.

How to apply Self-Consistency

Want to try this technique yourself? Here are some practical tips to get you started:

1. Identify the right perspectives

• Think about all the different angles that matter for your specific decision

• Ask yourself who'd have a stake in this decision in real life

• Pick perspectives that will show you different sides of the same problem

2. Craft your prompt carefully

• Tell the AI clearly that you want multiple perspective analysis

• Spell out what you want each perspective to focus on

• Ask for a final comparison that pulls all the insights together

3. Get the most from the results

• Look for patterns where different perspectives reach the same conclusion

• Pay special attention to contradictions—they're clues you need to dig deeper

• Use what you learn to make a more confident decision

When to use Self-Consistency

This approach works especially well when:

• You're wrestling with complex decisions that have many moving parts

• You really need an analysis you can trust

• You want to look at a problem from all angles

• You're trying to spot potential problems before they happen

But let's be real—not every question needs this much firepower. For simpler questions or straightforward decisions, a basic response will often do just fine.

That's it!

Self-Consistency is a game-changer that takes your AI conversations to a whole new level. It doesn't just give you more trustworthy answers—it helps you approach problems with the kind of structured, comprehensive thinking that leads to better decisions.

Next time you're facing a tough choice, try asking your AI to break it down from multiple angles. You'll be amazed at the insights you'll uncover!

Until next time,
G

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Note: This is part of my ongoing series on prompting techniques. I'll be exploring more powerful approaches in upcoming posts, so stay tuned!

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Hey! I'm Germán, and I write about AI in both English and Spanish. This article was first published in Spanish in my newsletter AprendiendoIA, and I've adapted it for my English-speaking friends at My AI Journey. My mission is simple: helping you understand and leverage AI, regardless of your technical background or preferred language. See you in the next one!