Experimental browser for the Atmosphere
Traditional prompt optimization is clearly missing a structured, scientific approach that will ensure reliability. Functional testing leverages automated input-output testing with multiple iterations and algorithmic scoring to turn prompt engineering into a measurable, data-driven process. #AI #ML
Mar 20, 2025, 2:53 PM
{ "uri": "at://did:plc:zwzdlsvtvlxnpuacwhmqmd22/app.bsky.feed.post/3lksxbfzohs2k", "cid": "bafyreify7lnfalm6yfxkdqmccfzcfktyayscterqoz7gkxy6ugwwiwl5tm", "value": { "text": "Traditional prompt optimization is clearly missing a structured, scientific approach that will ensure reliability.\nFunctional testing leverages automated input-output testing with multiple iterations and algorithmic scoring to turn prompt engineering into a measurable, data-driven process. #AI #ML", "$type": "app.bsky.feed.post", "embed": { "$type": "app.bsky.embed.external", "external": { "uri": "https://towardsdatascience.com/mastering-prompt-engineering-with-functional-testing-a-systematic-guide-to-reliable-llm-outputs/", "thumb": { "$type": "blob", "ref": { "$link": "bafkreidn6zcerlkozipk6hjdcipb5gffrwdyjhu3di3q5rwjcogekqgsru" }, "mimeType": "image/jpeg", "size": 319231 }, "title": "Mastering Prompt Engineering with Functional Testing: A Systematic Guide to Reliable LLM OutputsĀ | Towards Data Science", "description": "How prompt evaluation with a systematic approach composed of algorithmic testing with input/output data fixtures can make prompt engineering for complex AI tasks more reliable." } }, "langs": [ "en" ], "facets": [ { "index": { "byteEnd": 294, "byteStart": 291 }, "features": [ { "tag": "AI", "$type": "app.bsky.richtext.facet#tag" } ] }, { "index": { "byteEnd": 298, "byteStart": 295 }, "features": [ { "tag": "ML", "$type": "app.bsky.richtext.facet#tag" } ] } ], "createdAt": "2025-03-20T14:53:58.084Z" } }