Exciting news! LangSmith now offers dynamic few-shot example selectors, allowing you to rapidly improve LLM app performance with just one click. Say goodbye to manual dataset curation and hello to efficiency! 🚀 #LangSmith #LLMApps
https://blog.langchain.dev/dynamic-few-shot-examples-langsmith-datasets/
𝐂𝐨𝐩𝐢𝐥𝐨𝐭 𝐟𝐨𝐫 𝐒𝐞𝐜𝐮𝐫𝐢𝐭𝐲: 𝐞𝐥𝐞𝐦𝐞𝐧𝐭𝐬 𝐨𝐟 𝐚𝐧 𝐞𝐟𝐟𝐞𝐜𝐭𝐢𝐯𝐞 𝐩𝐫𝐨𝐦𝐩𝐭
From the "Get started with Microsoft Copilot for Security" online training, I highlight this interesting in-depth analysis.
𝐄𝐟𝐟𝐞𝐜𝐭𝐢𝐯𝐞 𝐩𝐫𝐨𝐦𝐩𝐭𝐬 give Copilot adequate and useful parameters to generate a valuable response. Security analysts or researchers should include the following elements when writing a prompt.
💡 𝐆𝐨𝐚𝐥 - specific, security-related information that you need
💡𝐂𝐨𝐧𝐭𝐞𝐱𝐭 - why you need this information or how you'll use it
💡𝐄𝐱𝐩𝐞𝐜𝐭𝐚𝐭𝐢𝐨𝐧𝐬 - format or target audience you want the response tailored to
💡𝐒𝐨𝐮𝐫𝐜𝐞 - known information, data sources, or plugins Copilot should use
At this link other prompting tips:
Full training: https://learn.microsoft.com/en-us/training/paths/security-copilot-and-ai/
#copilot #copilotforsecurity #securitycopilot #microsoft #microosoftsecurity #llm #openai #azureopenai #llmapps #soc #generativeai #genai #cybersecurity #azure #cloudsecurity #cloudnative #defender #sentinel #microsoftsentinel #xdr #defenderxdr #prompt #promptengineering
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