Best Practices for Complex Reasoning
SuNaAI Lab
Technical Guide Series
Discover how to unlock complex reasoning in LLMs through Chain-of-Thought prompting
Chain-of-Thought (CoT) prompting is one of the most powerful techniques for eliciting complex reasoning from language models. Instead of asking for a final answer directly, you guide the model to show its "thinking process" step-by-step, leading to more accurate and reliable results.
Research shows that CoT prompting can improve accuracy on complex tasks by up to 40%. By breaking down problems into intermediate steps, LLMs can tackle reasoning tasks that would otherwise be too difficult.
CoT leads to significantly higher accuracy on complex reasoning tasks like arithmetic, logical inference, and symbolic manipulation.
You can see the model's reasoning process, making it easier to debug, trust, and verify results.
CoT scales to problems that would be too difficult to solve in a single step.
By exposing intermediate reasoning, you get better confidence calibration and can identify uncertain steps.
A clear breakdown of what Chain-of-Thought prompting means and how it works
Chain-of-Thought (CoT) prompting is a technique that encourages language models to produce a sequence of intermediate reasoning steps leading to the final answer. Instead of jumping directly to a conclusion, the model first explains its thinking process.
❌ STANDARD PROMPT: "Q: A coffee shop sells 3 types of coffee. They sold 120 cups total. Type A is $3, Type B is $4, Type C is $5. If Type B sold 2x more than Type A, and Type C sold equal to Type A, how much revenue? A: $420" ✅ CHAIN-OF-THOUGHT PROMPT: "Q: A coffee shop sells 3 types of coffee. They sold 120 cups total. Type A is $3, Type B is $4, Type C is $5. If Type B sold 2x more than Type A, and Type C sold equal to Type A, how much revenue? Let's think step by step. A: Let me solve this step by step. Step 1: Define variables - Let A = cups of Type A sold - B = cups of Type B sold - C = cups of Type C sold Step 2: Write equations - A + B + C = 120 (total cups) - B = 2A (Type B sold 2x Type A) - C = A (Type C equal to Type A) Step 3: Solve for A A + 2A + A = 120 4A = 120 A = 30 Step 4: Solve for B and C B = 2(30) = 60 C = 30 Step 5: Calculate revenue Type A: 30 × $3 = $90 Type B: 60 × $4 = $240 Type C: 30 × $5 = $150 Total: $90 + $240 + $150 = $420 Final Answer: $420"
Decompose Complex Problems: Break down multi-step problems into smaller, manageable intermediate steps
Show Your Work: Explicitly show each reasoning step rather than skipping to conclusions
Use Natural Language: Express reasoning in clear, natural language that humans can understand and verify
Validate Each Step: Check intermediate results before proceeding to ensure the reasoning chain is sound
Understanding which tasks benefit most from CoT prompting
Not all tasks require Chain-of-Thought prompting. Understanding when to use it—and when not to— is crucial for effective prompt engineering. Here's a practical guide.
Word problems, algebra, calculus, multi-step arithmetic. CoT excels at these because the intermediate steps are clearly defined.
Puzzles, logical inference, constraint satisfaction. CoT helps track logical premises and conclusions systematically.
Multi-step analysis, statistical reasoning, trend identification. Break down complex analytical tasks into discrete steps.
Hypothesis testing, causal reasoning, experimental design. CoT helps structure scientific thinking processes.
Practical techniques for implementing effective CoT prompts
Simply add "Let's think step by step" or similar phrases to your prompt. This is the simplest form of CoT that works surprisingly well.
Question: If a train travels 60 miles in 1 hour, and another train travels 90 miles in 2 hours, which train is faster and by how much? Let's think step by step: Train 1: 60 miles / 1 hour = 60 mph Train 2: 90 miles / 2 hours = 45 mph Difference: 60 - 45 = 15 mph Answer: Train 1 is faster by 15 mph
Provide examples of CoT reasoning to teach the model the desired format. This is more reliable than zero-shot and works well for consistent formatting.
Example 1: Q: There are 15 trees in the park. Workers planted more trees today, and there are now 27 trees. How many trees were planted? A: Let me calculate: - Start: 15 trees - End: 27 trees - Difference: 27 - 15 = 12 Answer: 12 trees Example 2: Q: A library has 120 books. They bought 45 new books and donated 30 old books. How many books now? A: Let me calculate step by step: Step 1: Start with 120 books Step 2: Add 45 new books: 120 + 45 = 165 Step 3: Remove 30 books: 165 - 30 = 135 Answer: 135 books Now solve: Q: There are 8 students in a class. 3 joined and 1 left. How many students are there now?
Generate multiple reasoning paths and take the most frequent answer. This dramatically improves accuracy by reducing reasoning errors.
Proven strategies for effective Chain-of-Thought prompting
Clearly indicate what each step is doing. Use labels like "Step 1:", "First:", "Now:", or numbered lists to make reasoning transparent.
Check intermediate calculations before proceeding. This catches errors early and prevents cascading mistakes.
Express reasoning in clear, conversational language. Avoid overly formal or robotic phrasing. Natural explanations are easier to verify.
Don't break down every tiny calculation. Find the right balance between granularity and efficiency. Each step should be a meaningful unit of reasoning.
Indicate when steps are uncertain or when assumptions are being made. This provides better transparency and helps users understand model limitations.
Using CoT for trivial problems wastes tokens and increases latency without benefits.
Using unclear labels like "do this" instead of "calculate the total" makes reasoning hard to follow.
Not checking intermediate results leads to cascading errors that could have been caught early.
Failing to clearly state the final answer makes it hard to extract the result programmatically.
Generate multiple reasoning paths and evaluate them before choosing the best path. This is like exploring a search tree of possible solutions.
Use CoT to help models follow principles and constraints. Break down rules into reasoning steps that ensure compliance.
After generating an answer via CoT, have the model verify its own reasoning. This significantly reduces errors.