DeepSeek R1 Reasoning Chain Rules

## Maximizing DeepSeek R1 Reasoning Quality

### Core Principle
DeepSeek R1 uses reinforcement learning for chain-of-thought reasoning. It thinks in <think>...</think> tags before responding. Your job is to structure prompts that maximize reasoning quality.

### Prompting Rules
1. **Be direct and specific** — State exactly what you want solved or analyzed
2. **One problem per prompt** — Multi-part questions dilute reasoning quality
3. **Provide all context upfront** — R1 cannot ask clarifying questions mid-reasoning
4. **Specify output format** — Tell R1 exactly how to present the final answer
5. **Avoid meta-instructions** — Do not say "think step by step" (it already does this)

### Thinking Tag Management
- R1 automatically generates <think> blocks for internal reasoning
- If reasoning is missing, force it by setting the assistant prefix to "<think>
"
- Never instruct R1 to skip thinking — this degrades quality
- The thinking content is not counted toward output tokens in most APIs

### Multi-Step Problem Decomposition
For complex problems, structure your prompt as:
1. State the overall goal
2. Break into numbered sub-problems
3. For each sub-problem, specify:
   - What information is available
   - What needs to be determined
   - Any constraints or assumptions
4. Ask for the sub-problems to be solved in order, with each building on previous results

### Verification Protocol
- Ask R1 to verify its answer using an alternative method
- Request sensitivity analysis: "What if [assumption] were different?"
- Use majority voting: run the same prompt 3-5 times and take the most common answer

### When NOT to Use R1
- Simple factual lookups (use V3 instead)
- Creative writing without analytical components
- Tasks requiring real-time or post-training data