name

thought-based-reasoning

description

Use when tackling complex reasoning tasks requiring step-by-step logic, multi-step arithmetic, commonsense reasoning, symbolic manipulation, or problems where simple prompting fails - provides comprehensive guide to Chain-of-Thought and related prompting techniques (Zero-shot CoT, Self-Consistency, Tree of Thoughts, Least-to-Most, ReAct, PAL, Reflexion) with templates, decision matrices, and research-backed patterns

Thought-Based Reasoning Techniques for LLMs

Overview

Chain-of-Thought (CoT) prompting and its variants encourage LLMs to generate intermediate reasoning steps before arriving at a final answer, significantly improving performance on complex reasoning tasks. These techniques transform how models approach problems by making implicit reasoning explicit.

Quick Reference

Technique	When to Use	Complexity	Accuracy Gain
Zero-shot CoT	Quick reasoning, no examples available	Low	+20-60%
Few-shot CoT	Have good examples, consistent format needed	Medium	+30-70%
Self-Consistency	High-stakes decisions, need confidence	Medium	+10-20% over CoT
Tree of Thoughts	Complex problems requiring exploration	High	+50-70% on hard tasks
Least-to-Most	Multi-step problems with subproblems	Medium	+30-80%
ReAct	Tasks requiring external information	Medium	+15-35%
PAL	Mathematical/computational problems	Medium	+10-15%
Reflexion	Iterative improvement, learning from errors	High	+10-20%

Core Techniques

1. Chain-of-Thought (CoT) Prompting

Paper: "Chain of Thought Prompting Elicits Reasoning in Large Language Models" (Wei et al., 2022) Citations: 14,255+

When to Use

Multi-step arithmetic or math word problems
Commonsense reasoning requiring logical deduction
Symbolic reasoning tasks
When you have good exemplars showing reasoning

How It Works

Provide few-shot examples that include intermediate reasoning steps, not just question-answer pairs. The model learns to generate similar step-by-step reasoning.

Prompt Template

Q: Roger has 5 tennis balls. He buys 2 more cans of tennis balls. Each can has 3 tennis balls. How many tennis balls does he have now?
A: Roger started with 5 balls. 2 cans of 3 tennis balls each is 6 tennis balls. 5 + 6 = 11. The answer is 11.

Q: The cafeteria had 23 apples. If they used 20 to make lunch and bought 6 more, how many apples do they have?
A: The cafeteria had 23 apples originally. They used 20 to make lunch. So they had 23 - 20 = 3. They bought 6 more apples, so they have 3 + 6 = 9. The answer is 9.

Q: [YOUR QUESTION HERE]
A:

Strengths

Significant accuracy improvements on reasoning tasks
Interpretable intermediate steps
Works well with large models (>100B parameters)

Limitations

Requires crafting good exemplars
Less effective on smaller models
Can still make calculation errors

2. Zero-shot Chain-of-Thought

Paper: "Large Language Models are Zero-Shot Reasoners" (Kojima et al., 2022) Citations: 5,985+

When to Use

No exemplars available
Quick reasoning needed
General-purpose reasoning across task types
Prototyping before creating few-shot examples

How It Works

Simply append "Let's think step by step" (or similar phrase) to the prompt. This triggers the model to generate reasoning steps without any examples.

Prompt Template

Q: A juggler can juggle 16 balls. Half of the balls are golf balls, and half of the golf balls are blue. How many blue golf balls are there?

Let's think step by step.

Alternative trigger phrases:

"Let's work this out step by step to be sure we have the right answer."
"Let's break this down."
"Let's approach this systematically."
"First, let me understand the problem..."

Two-Stage Approach (More Robust)

Stage 1 - Reasoning Extraction:

Q: [QUESTION]
A: Let's think step by step.

Stage 2 - Answer Extraction:

[REASONING FROM STAGE 1]
Therefore, the answer is

Strengths

No exemplar crafting required
Generalizes across task types
Simple to implement

Limitations

Less effective than few-shot CoT
Can produce verbose or irrelevant reasoning
Sensitive to exact phrasing

3. Self-Consistency

Paper: "Self-Consistency Improves Chain of Thought Reasoning in Language Models" (Wang et al., 2022) Citations: 5,379+

When to Use

High-stakes decisions requiring confidence
Problems with multiple valid reasoning paths
When you need to reduce variance in outputs
Verification of reasoning correctness

How It Works

Sample multiple diverse reasoning paths, then select the most consistent answer via majority voting. The intuition: correct answers can be reached through multiple reasoning paths.

Prompt Template

[Use any CoT prompt - zero-shot or few-shot]

[Generate N samples with temperature > 0]

[Extract final answers from each sample]

[Return the most frequent answer (majority vote)]

Implementation Example

def self_consistency(prompt, n_samples=5, temperature=0.7):
    answers = []
    for _ in range(n_samples):
        response = llm.generate(prompt, temperature=temperature)
        answer = extract_answer(response)
        answers.append(answer)

    # Majority vote
    return Counter(answers).most_common(1)[0][0]

Strengths

Significant accuracy boost over single-path CoT
Provides confidence measure (agreement level)
Task-agnostic improvement

Limitations

Higher computational cost (N times more generations)
Requires extractable discrete answers
Diminishing returns beyond ~10-20 samples

4. Tree of Thoughts (ToT)

Paper: "Tree of Thoughts: Deliberate Problem Solving with Large Language Models" (Yao et al., 2023) Citations: 3,026+

When to Use

Complex problems requiring exploration/backtracking
Tasks where initial decisions are pivotal
Creative problem-solving (writing, puzzles)
When CoT alone achieves <50% accuracy

How It Works

Generalize CoT to a tree structure where each node is a "thought" (coherent language unit). Uses search algorithms (BFS/DFS) with self-evaluation to explore and select promising reasoning paths.

Prompt Template

Thought Generation:

Given the current state:
[STATE]

Generate 3-5 possible next steps to solve this problem.

State Evaluation:

Evaluate if the following partial solution is:
- "sure" (definitely leads to solution)
- "maybe" (could potentially work)
- "impossible" (cannot lead to solution)

Partial solution:
[THOUGHTS SO FAR]

BFS/DFS Search:

def tree_of_thoughts(problem, max_depth=3, beam_width=3):
    queue = [(problem, [])]  # (state, thought_path)

    while queue:
        state, path = queue.pop(0)

        if is_solved(state):
            return path

        # Generate candidate thoughts
        thoughts = generate_thoughts(state, k=5)

        # Evaluate and keep top-k
        evaluated = [(t, evaluate(state, t)) for t in thoughts]
        top_k = sorted(evaluated, key=lambda x: x[1])[:beam_width]

        for thought, score in top_k:
            if score != "impossible":
                new_state = apply_thought(state, thought)
                queue.append((new_state, path + [thought]))

    return None

Example: Game of 24

Problem: Use 4, 9, 10, 13 to get 24 (use +, -, *, / and each number once)

Thought 1: 13 - 9 = 4 (Now have: 4, 4, 10)
Evaluation: "maybe" - have two 4s and 10, could work

Thought 2: 10 - 4 = 6 (Now have: 4, 6, 13)
Evaluation: "maybe" - 4 * 6 = 24, need to use 13

Thought 3: 4 + 9 = 13 (Now have: 10, 13, 13)
Evaluation: "impossible" - no way to get 24 from these

Strengths

Dramatically improves performance on hard tasks (4% → 74% on Game of 24)
Enables backtracking and exploration
Self-evaluation catches errors early

Limitations

Significantly higher computational cost
Requires task-specific thought decomposition
Complex to implement

5. Least-to-Most Prompting

Paper: "Least-to-Most Prompting Enables Complex Reasoning in Large Language Models" (Zhou et al., 2022) Citations: 1,466+

When to Use

Problems harder than your exemplars
Compositional generalization tasks
Multi-step problems with clear subproblems
Symbol manipulation and SCAN-like tasks

How It Works

Two-stage process:

Decomposition: Break complex problem into simpler subproblems
Sequential Solving: Solve subproblems in order, using previous answers

Prompt Template

Stage 1: Decomposition

Q: Four years ago, Kody was only half as old as Mohamed. If Mohamed is currently twice as old as 30 years old, how old is Kody?

To solve "Four years ago, Kody was only half as old as Mohamed. If Mohamed is currently twice as old as 30 years old, how old is Kody?", we need to first solve:
- "If Mohamed is currently twice as old as 30 years old, how old is Mohamed?"
Then we need to solve:
- "Four years ago, Kody was only half as old as Mohamed. How old is Kody?"

Stage 2: Sequential Solving

Q: If Mohamed is currently twice as old as 30 years old, how old is Mohamed?
A: Mohamed is twice as old as 30, so Mohamed is 30 * 2 = 60 years old.

Q: Four years ago, Kody was only half as old as Mohamed. Mohamed is 60 years old. How old is Kody?
A: Four years ago, Mohamed was 60 - 4 = 56 years old. Kody was half as old, so Kody was 56 / 2 = 28 years old four years ago. Therefore, Kody is currently 28 + 4 = 32 years old.

The answer is 32.

Strengths

Excellent at generalizing to harder problems
Works well on compositional tasks
Explicit problem decomposition aids interpretability

Limitations

Requires two-stage prompting
Decomposition step can fail on novel structures
More complex setup than single-stage CoT

6. ReAct (Reasoning + Acting)

Paper: "ReAct: Synergizing Reasoning and Acting in Language Models" (Yao et al., 2022) Citations: 5,012+

When to Use

Tasks requiring external information (search, APIs)
Interactive decision-making environments
Multi-hop question answering
When pure reasoning leads to hallucination

How It Works

Interleave reasoning traces ("Thought") with actions ("Action") and observations ("Observation"). Reasoning helps plan actions; actions provide new information for reasoning.

Prompt Template

Question: What is the elevation range for the area that the eastern sector of the Colorado orogeny extends into?

Thought 1: I need to search Colorado orogeny, find the area that the eastern sector extends into, then find the elevation range of that area.
Action 1: Search[Colorado orogeny]
Observation 1: The Colorado orogeny was an episode of mountain building (an orogeny) in Colorado and surrounding areas.

Thought 2: It does not mention the eastern sector. So I need to look up eastern sector.
Action 2: Lookup[eastern sector]
Observation 2: The eastern sector extends into the High Plains.

Thought 3: The eastern sector extends into the High Plains. I need to search High Plains and find its elevation range.
Action 3: Search[High Plains]
Observation 3: High Plains refers to one of two distinct land regions: High Plains (United States)...

Thought 4: I need to search High Plains (United States).
Action 4: Search[High Plains (United States)]
Observation 4: The High Plains are a subregion of the Great Plains... elevation of around 1,800 to 7,000 ft (550 to 2,130 m).

Thought 5: The elevation range is 1,800 to 7,000 ft.
Action 5: Finish[1,800 to 7,000 ft]

Action Types

Search[query] - Search for information
Lookup[keyword] - Look up keyword in current context
Finish[answer] - Return final answer

Strengths

Reduces hallucination by grounding in external knowledge
Interpretable action traces
Handles exceptions through adaptive reasoning

Limitations

Requires integration with external tools
More complex orchestration
Action space must be defined

7. PAL (Program-Aided Language Models)

Paper: "PAL: Program-aided Language Models" (Gao et al., 2022) Citations: 608+

When to Use

Mathematical/arithmetic reasoning
Problems requiring precise computation
Symbolic manipulation
When CoT makes calculation errors

How It Works

Generate code (typically Python) instead of natural language reasoning. Execute the code to get the answer. The LLM handles decomposition; the interpreter handles computation.

Prompt Template

Q: Roger has 5 tennis balls. He buys 2 more cans of tennis balls. Each can has 3 tennis balls. How many tennis balls does he have now?

# solution in Python:
def solution():
    """Roger has 5 tennis balls. He buys 2 more cans of tennis balls. Each can has 3 tennis balls. How many tennis balls does he have now?"""
    tennis_balls_initial = 5
    bought_cans = 2
    tennis_balls_per_can = 3
    tennis_balls_bought = bought_cans * tennis_balls_per_can
    tennis_balls_total = tennis_balls_initial + tennis_balls_bought
    return tennis_balls_total

Q: The bakers at the Beverly Hills Bakery baked 200 loaves of bread on Monday morning. They sold 93 loaves in the morning and 39 loaves in the afternoon. A grocery store returned 6 unsold loaves. How many loaves of bread did they have left?

# solution in Python:
def solution():
    """The bakers baked 200 loaves. They sold 93 in morning, 39 in afternoon. A store returned 6. How many left?"""
    loaves_baked = 200
    loaves_sold_morning = 93
    loaves_sold_afternoon = 39
    loaves_returned = 6
    loaves_left = loaves_baked - loaves_sold_morning - loaves_sold_afternoon + loaves_returned
    return loaves_left

Strengths

Eliminates arithmetic errors
Clear variable naming aids interpretability
Leverages code execution for verification

Limitations

Requires code interpreter
Not suitable for non-computational reasoning
Model must generate syntactically correct code

8. Auto-CoT

Paper: "Automatic Chain of Thought Prompting in Large Language Models" (Zhang et al., 2022) Citations: 838+

When to Use

No manually crafted exemplars available
Want to automate few-shot CoT setup
Scaling CoT to many tasks
When zero-shot CoT isn't sufficient

How It Works

Cluster questions by diversity
Use Zero-shot CoT to generate reasoning chains for representative questions
Use these auto-generated chains as few-shot exemplars

Prompt Template

Step 1: Generate diverse demonstrations

# Cluster questions
clusters = cluster_questions(all_questions, k=8)

# For each cluster, pick representative and generate CoT
demonstrations = []
for cluster in clusters:
    question = select_representative(cluster)
    reasoning = zero_shot_cot(question)  # "Let's think step by step"
    demonstrations.append((question, reasoning))

Step 2: Use as few-shot exemplars

Q: [Demo question 1]
A: Let's think step by step. [Generated reasoning 1]

Q: [Demo question 2]
A: Let's think step by step. [Generated reasoning 2]

...

Q: [New question]
A: Let's think step by step.

Strengths

No manual exemplar creation
Diversity sampling improves robustness
Matches manual CoT performance

Limitations

Quality depends on zero-shot CoT quality
Clustering requires similarity metric
Some generated chains contain errors

9. Reflexion

Paper: "Reflexion: Language Agents with Verbal Reinforcement Learning" (Shinn et al., 2023) Citations: 2,179+

When to Use

Iterative improvement over multiple attempts
Learning from errors without fine-tuning
Complex coding or decision-making tasks
When single-pass reasoning is insufficient

How It Works

After task failure, the agent generates a verbal "reflection" analyzing what went wrong. This reflection is stored in memory and used in subsequent attempts to avoid repeating mistakes.

Prompt Template

Initial Attempt:

Task: [TASK DESCRIPTION]

Thought: [REASONING]
Action: [ACTION]
...
Result: [FAILURE/PARTIAL SUCCESS]

Reflection:

The previous attempt failed because:
1. [SPECIFIC ERROR ANALYSIS]
2. [WHAT SHOULD HAVE BEEN DONE]
3. [KEY INSIGHT FOR NEXT ATTEMPT]

Reflection: In the next attempt, I should...

Subsequent Attempt (with memory):

Task: [TASK DESCRIPTION]

Previous reflections:
- [REFLECTION 1]
- [REFLECTION 2]

Using these insights, I will now attempt the task again.

Thought: [IMPROVED REASONING]
Action: [BETTER ACTION]

Example: Code Generation

Task: Write a function to find the longest palindromic substring.

Attempt 1: [CODE WITH BUG]
Test Result: Failed on "babad" - expected "bab" or "aba", got "b"

Reflection: My solution only checked single characters. I need to:
1. Consider substrings of all lengths
2. Use expand-around-center technique for efficiency
3. Track both start position and maximum length

Attempt 2: [IMPROVED CODE USING REFLECTION]
Test Result: Passed all tests

Strengths

Learns from errors without weight updates
Achieves 91% on HumanEval (surpassing GPT-4's 80%)
Builds episodic memory of insights

Limitations

Requires multiple attempts
Memory management for long sessions
Quality of reflection affects improvement

Decision Matrix: Which Technique to Use

                           Need Examples?
                          /              \
                        No                Yes
                        |                  |
                Zero-shot CoT          Few-shot CoT
                        |                  |
                Need higher accuracy?  Need computation?
                /                \           |
              Yes               No          PAL
               |                |
    Self-Consistency    Done with CoT
               |
        Still not enough?
        /              \
      Yes              No
       |                |
  Problem decomposable?  Done
  /                    \
Yes                    No
 |                      |
Least-to-Most     Need exploration?
                  /              \
                Yes              No
                 |                |
          Tree of Thoughts   Need external info?
                             /              \
                           Yes              No
                            |                |
                          ReAct         Need iteration?
                                        /           \
                                      Yes           No
                                       |             |
                                   Reflexion      Use CoT

Best Practices

1. Start Simple

Begin with Zero-shot CoT ("Let's think step by step"), then progress to more complex techniques if needed.

2. Match Technique to Task

Math/Logic: CoT, PAL, Self-Consistency
Multi-hop QA: ReAct, Least-to-Most
Creative/Puzzles: Tree of Thoughts
Iterative Tasks: Reflexion

3. Combine Techniques

Techniques are often complementary:

ReAct + Self-Consistency for robust factual answers
ToT + PAL for complex computational exploration
Least-to-Most + Reflexion for hard multi-step problems

4. Prompt Engineering Tips

Use clear step markers ("Step 1:", "First,", etc.)
Include diverse exemplars covering edge cases
Format consistently across examples
Add verification steps ("Let me verify...")

Common Mistakes

Mistake	Why It's Wrong	Fix
Using CoT for simple lookups	Adds unnecessary tokens and latency	Reserve for multi-step reasoning
Too few samples in Self-Consistency	Majority voting needs adequate samples	Use 5-10 samples minimum
Generic "think step by step" without checking output	Model may produce irrelevant reasoning	Validate reasoning quality, not just presence
Mixing techniques without understanding trade-offs	Computational cost without benefit	Understand when each technique adds value
Using PAL without code interpreter	Code generation is useless without execution	Ensure execution environment available
Not testing exemplar quality in few-shot CoT	Poor exemplars lead to poor reasoning	Validate exemplars solve problems correctly
Applying Tree of Thoughts to linear problems	Massive overhead for no benefit	Use ToT only when exploration needed

References

Wei, J. et al. (2022). "Chain of Thought Prompting Elicits Reasoning in Large Language Models." arXiv:2201.11903
Kojima, T. et al. (2022). "Large Language Models are Zero-Shot Reasoners." arXiv:2205.11916
Wang, X. et al. (2022). "Self-Consistency Improves Chain of Thought Reasoning in Language Models." arXiv:2203.11171
Yao, S. et al. (2023). "Tree of Thoughts: Deliberate Problem Solving with Large Language Models." arXiv:2305.10601
Zhou, D. et al. (2022). "Least-to-Most Prompting Enables Complex Reasoning in Large Language Models." arXiv:2205.10625
Yao, S. et al. (2022). "ReAct: Synergizing Reasoning and Acting in Language Models." arXiv:2210.03629
Gao, L. et al. (2022). "PAL: Program-aided Language Models." arXiv:2211.10435
Zhang, Z. et al. (2022). "Automatic Chain of Thought Prompting in Large Language Models." arXiv:2210.03493
Shinn, N. et al. (2023). "Reflexion: Language Agents with Verbal Reinforcement Learning." arXiv:2303.11366

Install Skill

SKILL.md

Thought-Based Reasoning Techniques for LLMs

Overview

Quick Reference

Core Techniques

1. Chain-of-Thought (CoT) Prompting

When to Use

How It Works

Prompt Template

Strengths

Limitations

2. Zero-shot Chain-of-Thought

When to Use

How It Works

Prompt Template

Two-Stage Approach (More Robust)

Strengths

Limitations

3. Self-Consistency

When to Use

How It Works

Prompt Template

Implementation Example

Strengths

Limitations

4. Tree of Thoughts (ToT)

When to Use

How It Works

Prompt Template

Example: Game of 24

Strengths

Limitations

5. Least-to-Most Prompting

When to Use

How It Works

Prompt Template

Strengths

Limitations

6. ReAct (Reasoning + Acting)

When to Use

How It Works

Prompt Template

Action Types

Strengths

Limitations

7. PAL (Program-Aided Language Models)

When to Use

How It Works

Prompt Template

Strengths

Limitations

8. Auto-CoT

When to Use

How It Works

Prompt Template

Strengths

Limitations

9. Reflexion

When to Use

How It Works

Prompt Template

Example: Code Generation

Strengths

Limitations

Decision Matrix: Which Technique to Use

Best Practices

1. Start Simple

2. Match Technique to Task

3. Combine Techniques

4. Prompt Engineering Tips

Common Mistakes

References