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State-Of-The-Art Prompting For AI Agents
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05/30/25
Takeaways
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Metaprompting is a powerful tool that feels like coding in the early days of programming, where the tools are still evolving. It requires learning how to effectively communicate with AI to help it make good decisions.
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The 'lean startup' approach helps startups validate ideas quickly.
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The best AI startups are actively refining their prompt engineering practices, gathering insights from numerous companies to improve the quality and reliability of AI outputs.
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Parahelp excels in AI customer support, powering services for top AI companies like Perplexity and Replit. They shared their detailed prompt, which outlines the role of the AI as a customer service agent and specifies tasks clearly.
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Effective prompts often start by defining the role of the AI, detailing its responsibilities, and providing a structured plan for tasks, which helps in guiding the AI's decision-making process.
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The best prompts utilize a markdown-style format to organize information, making it easier for AI to follow instructions and integrate with other systems.
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In prompt engineering, it's crucial to provide examples of desired outputs, as this helps the AI reason through complex tasks and improves the overall quality of its responses.
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When prompts become too complex, using examples can be more effective than trying to articulate every detail in prose, similar to test-driven development in programming.
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To prevent AI from generating misleading information, it's important to give it an escape hatch, allowing it to indicate when it lacks sufficient information to make a decision.
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One innovative approach to providing AI with an escape hatch is to allow it to express confusion or request clarification when faced with ambiguous instructions, which can help improve the quality of its outputs.
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Meta prompting involves giving a role to the AI, such as an expert prompt engineer, to enhance the quality of prompts.
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Using a larger model for meta prompting can yield better prompts that are then distilled into smaller models for practical use.
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In voice AI, reducing latency is crucial; a faster model with a refined prompt can help pass the Turing test by minimizing response delays.
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Documenting issues and improvements in a Google Doc while using AI can help refine prompts effectively when fed back into the AI.
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The thinking traces in Gemini Pro provide critical debug information to understand prompt failures and improve them.
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Evals are considered the true crown jewel data asset for companies, as they provide context for why prompts were designed a certain way.
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Founders need to understand their users deeply, as this knowledge is essential for creating effective software solutions.
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Every founder has become a forward deployed engineer, meaning they must work closely with users to understand their needs and workflows.
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Palantir's approach of sending engineers to work alongside clients, rather than salespeople, has been key to securing large contracts.
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Startup founders should act as forward deployed engineers, engaging directly with customers to gather feedback and iterate quickly on their products.
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Founders must be hands-on, embodying roles such as product designer and ethnographer, to effectively communicate and demonstrate their solutions to potential clients.
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Vertical AI agents are thriving because they can have direct meetings with end buyers, quickly adapting their prompts based on real-time feedback.
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The forward deployed engineer model allows startups to close significant deals with large enterprises by demonstrating innovative solutions that meet customer needs.
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Different AI models exhibit distinct personalities; for instance, Claude is more human-like and steerable, while Lama 4 requires more guidance and is less flexible.
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Using scoring rubrics for evaluating potential investors can help founders determine which investors to engage with, balancing strict criteria with flexibility for exceptions.
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The effectiveness of AI models can vary significantly based on how they interpret and apply rubrics, with some being rigid and others more adaptable to exceptions.
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The Kaizen principle emphasizes that those directly involved in a process are best suited to improve it, which is applicable to both manufacturing and AI prompting.