Two AI Thinking Hacks That Could Supercharge Intelligence
TL;DR:
Two techniques are quietly changing how we work with large language models: Self-Consistency and Tree of Thoughts (ToT). They both aim to solve a common problem—LLMs being too quick to give a single (sometimes wrong) answer. These approaches help AI reason more like humans: slowly, step-by-step, with checks, branches, and better outcomes. If you’re working with OpenAI, Anthropic, or any other advanced model, they’re well worth understanding.
AI has made huge strides in generating fluent, natural-sounding responses. But here’s the thing: sounding smart and being smart aren’t the same.
If you’ve ever used a language model and gotten an answer that sounded confident but turned out to be wrong, you’ve run into a core limitation of how these systems reason. They tend to go with the first idea that “sounds right” and commit to it — no matter how shaky the logic might be.
That’s where techniques like Self-Consistency and Tree of Thoughts come in. They don’t change the model itself — they change how we prompt and interact with it to get smarter, more robust results. And best of all, you don’t need to be a researcher to use them.
What Is Self-Consistency in Prompt Engineering?
Let’s start with Self-Consistency, because it’s the easier of the two to implement.
Ever asked a model the same question twice — and gotten two different answers? That variability can be frustrating, but it’s also an opportunity. Self-Consistency takes advantage of that randomness in a smart way.
How It Works:
Instead of relying on a single answer, you ask the model to generate multiple reasoning paths using something like a Chain of Thought prompt (more on that in a second). Then you look at all the answers and pick the one that shows up the most.
Yep — it’s basically a majority vote.
Here’s how you do it:
Prompt the model with a step-by-step reasoning format (e.g., “Let’s think step by step…”).
Sample multiple responses — say, 5 to 10.
Pick the most frequent answer.
If most of them say “42,” and one says “17,” you can safely assume “42” is more likely to be right.
Why This Works So Well
Filters out randomness and errors Instead of rolling the dice on a single pass, you get to see what the model usually says when it thinks it through.
Better for logic-heavy tasks Self-Consistency shines in math, data analysis, logical reasoning — anything where there’s one correct answer and the path to it matters.
Simple to apply You don’t need a plugin or a special API. Just use the same prompt multiple times and tally the results.
Real-World Example
Prompt: “If you have 3 apples and buy 2 more, how many apples do you have?”
Responses:
“3 + 2 = 5 apples”
“Three apples plus two more is five.”
“The answer is 5.”
“You end up with 5 apples.”
“There are now 5 apples.”
Even if one rogue answer says 4 or 6, it’s clear what the consensus is.
Self-Consistency helps smooth out the noise — and that’s powerful when you’re making decisions based on AI outputs.
What Is Tree of Thoughts (ToT) in AI Reasoning?
Now let’s talk about Tree of Thoughts, which builds on Chain of Thought but adds something new: the ability to branch out, explore alternatives, and backtrack — just like humans do when we’re problem-solving.
Instead of a straight line of reasoning, Tree of Thoughts lets the model consider multiple paths at each step, compare them, and choose the most promising one.
Think of it like a brainstorm where nothing is off the table… at first.
How Tree of Thoughts Works:
Break the problem into steps: The model starts by chunking the task into smaller “thoughts” or subtasks.
Generate multiple continuations per step: Instead of one follow-up, it tries out 2–3 ideas for each sub-step — forming a branching tree of possibilities.
Evaluate which branches are working: The model then checks its progress and scores each path.
Prune weak paths and keep exploring: Bad branches get trimmed. Good ones keep growing.
Backtrack if needed: If the AI hits a dead end, it can return to earlier steps and try a different path.
Why Tree of Thoughts Is a Game-Changer
Mimics real human thinking: We rarely solve hard problems in one straight line. We try, fail, reconsider, and iterate. Tree of Thoughts gives AI that same flexibility.
Handles complexity better: Perfect for multi-step planning, creative ideation, or logic puzzles where one bad assumption can ruin the whole chain.
Unlocks new use cases: You can apply this in design, strategy, code generation, long-form writing — anywhere you want the AI to explore options instead of guessing.
Quick Example
Let’s say you ask a model:
“Design a plan to reduce customer churn by 20% in Q3.”
With Tree of Thoughts:
One branch might explore pricing changes.
Another might look at loyalty programs.
A third might suggest onboarding improvements.
Other practical examples include:
Example 1: Increase Online Store Sales
Goal: Increase online store sales by 15% this quarter.
Step 1: Break the problem into smaller tasks.
Step 2: For each task, generate three alternative approaches.
Step 3: Briefly evaluate each approach for potential impact and feasibility.
Step 4: Select the most promising option before moving to the next task.
Final Step: Summarize the chosen path and outline the final plan.
Example 2: Write the Opening Scene of a Mystery Novel
Step 1: Brainstorm three different setting ideas.
Step 2: Evaluate each setting for atmosphere and intrigue.
Step 3: Choose the strongest setting.
Step 4: Expand it into a 300-word scene using vivid detail and suspense.
Each path gets explored in detail, evaluated, and refined. Then the model picks the best one — or combines elements of several.
Compare that to a flat, one-shot response that just guesses and runs with it. No contest.
Final Thoughts
These techniques don’t make the AI itself smarter — they help you use it more intelligently.
That’s the real shift happening right now in AI: not just building better models, but learning better ways to interact with them. And the more we treat prompting like a craft — like writing or UX — the better our results get.
So next time you're trying to solve something tricky with an AI assistant, don’t settle for the first answer it gives you. Ask again. Explore alternatives. Vote on responses. Guide the process.
The tools are getting better. The prompts should too.
Have you tried Self-Consistency or Tree of Thoughts in your AI projects? I’d love to hear what’s worked, what hasn’t, and where you’re pushing the boundaries.
Drop me a note or connect — always down to chat with fellow builders, researchers, and AI nerds.
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