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Sunday, 26 May 2024

Data Machina #254

Substack 2 years ago 31

Princeton Language & Intelligence released SWE-bench, a benchmark for evaluating AI coding agents on their ability to fix real GitHub repository issues, revealing that current AI agents perform poorly on the task. The leading model, Amazon Q Developer Agent, successfully solved only 13.8% of 2294 tasks, while the open-source OpenDevin achieved the highest benchmark score at 21%. These results demonstrate that despite multiple competing approaches including Devin, Devika, and GPT-Engineer, AI coding agents remain far from ready for production-scale legacy code migration and autonomous software engineering work.

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AI Coding Agents

Prompting Fundamentals and How to Apply them Effectively

Eugene Yan 2 years ago 30

The article explains fundamental techniques for writing effective prompts for large language models, including assigning roles, using structured input/output formats, prefilling responses, and providing multiple examples. Key techniques discussed include using XML tags for structure, providing at least 12 examples in n-shot prompting (not just 3-5), and matching example distributions to production data. The practical application of these fundamentals helps LLM users obtain more reliable and consistent outputs through better conditioning of the model's probabilistic behavior.

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