{"id":6345,"date":"2025-08-26T07:02:23","date_gmt":"2025-08-26T07:02:23","guid":{"rendered":"https:\/\/mailitics.com\/index.php\/2025\/08\/26\/systematic-llm-prompt-engineering-using-dspy-optimization\/"},"modified":"2025-08-26T07:02:23","modified_gmt":"2025-08-26T07:02:23","slug":"systematic-llm-prompt-engineering-using-dspy-optimization","status":"publish","type":"post","link":"https:\/\/mailitics.com\/index.php\/2025\/08\/26\/systematic-llm-prompt-engineering-using-dspy-optimization\/","title":{"rendered":"Systematic LLM Prompt Engineering Using DSPy Optimization"},"content":{"rendered":"<p>    Systematic LLM Prompt Engineering Using DSPy Optimization<br \/>\n \t<BR><br \/>\n<BR><\/BR><br \/>\n    <!-- no image --><br \/>\n \t<BR><br \/>\n<BR><\/BR><\/p>\n<div>\n<p>This article is a journey into the fascinating and rapidly evolving science of LLM prompt iteration, which is a fundamental part of Large Language Model Operations (LLMOPs). We\u2019ll use the example of generating customer service responses with a real-world dataset to show how both generator and LLM-judge prompts can be developed in a systematic fashion [\u2026]<\/p>\n<p>The post <a href=\"https:\/\/towardsdatascience.com\/systematic-llm-prompt-engineering-using-dspy-optimization\/\">Systematic LLM Prompt Engineering Using DSPy Optimization<\/a> appeared first on <a href=\"https:\/\/towardsdatascience.com\/\">Towards Data Science<\/a>.<\/p>\n<\/div>\n<p> \t<BR><br \/>\n <BR><\/BR><br \/>\n    Robert Martin-Short<br \/>\n \t<BR><br \/>\n<BR><\/BR><br \/>\n<a href=\"https:\/\/towardsdatascience.com\/systematic-llm-prompt-engineering-using-dspy-optimization\/\">Go to original source<\/a><br \/>\n \t<BR><br \/>\n <BR><\/BR><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Systematic LLM Prompt Engineering Using DSPy Optimization This article is a journey into the fascinating and rapidly evolving science of LLM prompt iteration, which is a fundamental part of Large Language Model Operations (LLMOPs). We\u2019ll use the example of generating customer service responses with a real-world dataset to show how both generator and LLM-judge prompts [&hellip;]<\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[62,69,3039,240,71,3587,1771],"tags":[134,2045,3588],"class_list":["post-6345","post","type-post","status-publish","format-standard","hentry","category-aimldsaimlds","category-artificial-intelligence","category-dspy","category-editors-pick","category-large-language-models","category-llmops","category-prompt-engineering","tag-llm","tag-prompt","tag-systematic"],"_links":{"self":[{"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/posts\/6345"}],"collection":[{"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/comments?post=6345"}],"version-history":[{"count":0,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/posts\/6345\/revisions"}],"wp:attachment":[{"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/media?parent=6345"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/categories?post=6345"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/tags?post=6345"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}