Beyond the Basics
Advanced techniques unlock more capable and reliable AI behavior for complex tasks.
Chain of Thought
Encourage step-by-step reasoning by asking the model to "think through" problems.
Example: "Let's solve this step by step..."
Important: CoT is Not Universal
2025 research shows Chain of Thought prompting is NOT universally beneficial. While it significantly improves performance on reasoning-heavy tasks, it provides minimal gains for non-reasoning tasks.
- -Most effective for math, logic, and multi-step reasoning problems
- -Minimal benefit for simple retrieval, classification, or creative tasks
- -Increases latency and token costs—use strategically, not by default
Few-Shot Learning
Provide multiple examples to establish patterns the model should follow.
Include 3-5 diverse examples covering edge cases.
Self-Consistency
Generate multiple responses and select the most consistent answer.
Useful for math, logic, and factual questions.
Task Decomposition
Break complex tasks into smaller, manageable sub-tasks.
Solve sub-tasks independently, then combine results.
Tree of Thoughts (ToT)
An extension of Chain of Thought that explores multiple reasoning paths simultaneously, evaluating and backtracking when needed to find optimal solutions.
How It Works
Generate multiple reasoning branches at each step. Evaluate promising paths, prune dead ends, and backtrack to explore alternatives.
Best For
Planning problems, puzzles, creative tasks requiring exploration, and problems where the first approach may not be optimal.
Example: "Consider 3 different approaches to solve this. For each, think 2 steps ahead. Evaluate which path is most promising, then continue."
Graph of Thoughts (GoT)
A non-linear reasoning structure where thoughts can merge, branch, and form cycles—modeling how humans actually think about complex problems.
Key Feature
Unlike linear CoT or tree-structured ToT, GoT allows combining insights from different reasoning paths and revisiting earlier conclusions.
Best For
Complex problems with interdependencies, synthesis tasks, and problems where partial solutions need to be combined.
Example: "Analyze this problem from angles A, B, and C independently. Then identify connections between your analyses and synthesize a unified solution."
Cost-Benefit Analysis
Advanced prompting techniques increase token usage, latency, and API costs. Understanding when these tradeoffs are worthwhile is crucial for production systems.
Worth the Extra Cost
- +Complex reasoning: math, logic, multi-step analysis
- +High-stakes decisions: medical, legal, financial advice
- +Problems where accuracy matters more than speed
Often Not Worth It
- -Simple classification or extraction tasks
- -High-volume, latency-sensitive applications
- -Tasks where simpler prompts already achieve high accuracy
Tip: Start with simple prompts and add complexity only when needed. Measure the accuracy improvement against the cost increase to make informed decisions.
Additional Techniques
Role Assignment
Assign a specific expert persona to focus the model's knowledge.
Explicit Constraints
List what the model should NOT do to prevent common errors.
Self-Verification
Ask the model to check its own work for errors.
Chain of Thought Demo
See how reasoning steps improve outputs
Select Problem
See how chain-of-thought improves reasoning
If a store has 847 apples and sells 3/7 of them, then receives a shipment of 156 more apples, how many apples does the store have?
The store has 639 apples.
Without reasoning steps, the model may jump to an intuitive but wrong answer.
Click "Show Steps" to see how chain-of-thought reasoning leads to the correct answer.
Chain-of-thought prompting asks the model to "think step by step." This explicit reasoning process helps catch errors that intuitive jumping would miss, especially in math, logic, and multi-step problems.
Key Takeaways
- 1Chain of thought improves reasoning tasks, but is not universally beneficial
- 2Few-shot examples establish reliable patterns
- 3ToT and GoT extend CoT for complex, non-linear problems
- 4Always consider cost vs. benefit—advanced techniques increase token usage
- 5Start simple, add complexity only when accuracy requires it