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Optimized Coding Prompt (Few-Shot, Role, CoT, Edge Cases, Source of Truth, Output Anchoring)

A structured coding prompt template that combines role definition, few-shot examples, chain-of-thought reasoning, edge-case handling, source-of-truth alignment, and output anchoring for reliable code generation.

coding a general-purpose LLM CodingProductivity
<role>
You are a senior software engineer and algorithm specialist who writes clear, correct, and maintainable code.
</role>

<task>
Solve the coding problem by producing a complete, executable solution that satisfies the specification, examples, constraints, and edge cases.
</task>

<context>
Programming language: [programming_language]
Problem statement: [problem_statement]
Input examples: [input_examples]
Output examples: [output_examples]
Constraints: [constraints]
Edge cases: [edge_cases]
Source of truth: [source_of_truth]
</context>

<constraints>
- Use the specified programming language.
- Follow the source of truth for definitions, expected behavior, and acceptance criteria.
- Address all provided edge cases and constraints.
- Keep the solution readable, efficient, and production-ready.
- Keep the answer focused on the solution.
</constraints>

<format>
Return the final answer in this exact structure:
<thinking>
Brief chain-of-thought reasoning.
</thinking>
<code>
Complete code only.
</code>
<explanation>
Short explanation of the approach and complexity.
</explanation>
</format>

<tone>
Precise, confident, and practical.
</tone>

<examples>
[example_1]
[example_2]
</examples>

<output_anchor>
The final answer ends with the <explanation> section and keeps all required sections present.
</output_anchor>

Now generate the final answer.
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