Important study in this paper. If prompt engineering techniques is relevant for advanced reasoning-based model like OpenAI's 01-preview.
It finds advanced reasoning-based LLMs make traditional prompt engineering obsolete for most software tasks.
These news models (like 01-preview), need less prompt engineering, more direct instructions
📚 https://arxiv.org/abs/2411.02093
🎯 Original Problem:
Advanced LLMs like OpenAI's 01-preview have emerged, questioning whether traditional prompt engineering techniques remain necessary for software engineering tasks. Engineers need clarity on whether to invest time in complex prompting or rely on model capabilities alone.
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🛠️ Solution in this Paper:
→ Conducted extensive empirical study across three key software tasks: code generation, code translation, and code summarization
→ Evaluated 11 state-of-the-art approaches using various prompt engineering techniques
→ Compared performance between reasoning models (Claude) and non-reasoning models (GPT-4)
→ Used standard datasets: HumanEval for generation, CodeTrans for translation, and CodeSearchNet for summarization
→ Analyzed cost-effectiveness and environmental impact of different model choices
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💡 Key Insights:
→ Complex prompt engineering techniques often show reduced benefits with advanced LLMs
→ Simple zero-shot prompts work better with reasoning models than elaborate prompting
→ Reasoning models excel at complex tasks but offer minimal advantages for simpler ones
→ Non-reasoning models are more cost-effective for basic tasks
→ Output format control is crucial when using reasoning LLMs
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📊 Results:
→ Prompt engineering techniques developed for earlier LLMs showed diminished benefits or negative impacts on advanced models
→ Reasoning LLMs performed better in multi-step tasks but showed minimal advantages in simpler tasks
→ Non-reasoning models proved more practical for tasks not requiring complex reasoning
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