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GPT-4.1: Optimizing Agentic Workflows with Prompting
- GPT-4.1 excels in agentic workflows due to fine-tuning on diverse problem-solving paths, high instruction fidelity, improved tool usage (even outperforming some reasoning-focused models on SWE-bench Verified), and prompt steerability.
- Specific prompting strategies enhance GPT-4.1's agentic capabilities: Persistence prompting prevents premature termination ("Only terminate your turn when you are sure that the problem is solved."), tool-calling instructions encourage tool usage over hallucination ("Use your tools to read files and gather information; do NOT guess."), and planning prompts promote chain-of-thought reasoning.
- Tool API integration significantly improves performance: Passing tools via the tools API parameter instead of inline prompt descriptions improves SWE-bench Verified scores by 2%. Explicit planning with chain-of-thought prompting further increases SWE-bench task performance by 4%.
- OpenAI's structured system prompt template (including workflow, strategy, testing, verification, and tool usage instructions) substantially improves performance metrics (nearly 20%) in agentic settings.
- _According to additional source 2, GPT-4 Turbo_, a related model, features a 128K context window, knowledge up to April 2023, and new API features like JSON mode and reproducible outputs via a `seed` parameter. It also shows improved accuracy and reduced laziness compared to previous GPT-4 models.
- _According to additional source 3, OpenAI's GPT-4 Prompting Guide_ emphasizes clear, specific instructions, context provision, delimiters for input separation, specified output formats (e.g., JSON), few-shot prompting, and chain-of-thought prompting to enhance response quality and reliability while mitigating hallucinations and biases.
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