SPWe can add multiple range and the remove them together. The point is instructions will matter with a group of ranges.Reply·Article·Oct 16·AI Skill Test Coverage: Can we create line coverage for AI skills Eval?
SPGreat point. Will look into it.Reply·Article·Oct 16·AI Skill Test Coverage: Can we create line coverage for AI skills Eval?
SPhttps://blog.subhampanda.com/ai-skill-test-coverage-can-we-create-line-coverage-for-ai-skills-eval The above article answers your question.Reply·Article·Oct 15·1·Layered Skill Test Coverage: A Better Way to Evaluate AI Agent Skills
SPFor more information: https://blog.subhampanda.com/ai-skill-test-coverage-can-we-create-line-coverage-for-ai-skills-evalReply·Article·Oct 15·1·Layered Skill Test Coverage: A Better Way to Evaluate AI Agent Skills
SPYes the first part would be on the task list. But the second part of the paper to detext mutation we can use it to get line by line coverage of a skill. I will create a detailed blog post on that today.Reply·Article·Oct 15·1·Layered Skill Test Coverage: A Better Way to Evaluate AI Agent Skills
SPYes I am going a bit deep into the analysis like how the llm would behave when I use a large monolithic context that would be equivalent to the context size. Would post the analysis once I am done compiling the results.Reply·Article·Jun 12·1·Progressive Disclosure vs. Monolithic Skills: A Real-World Benchmark of How to Structure LLM Instructions
SPhttps://blog.subhampanda.com/word2vec-demystified-from-one-hot-to-vector-magic-cbow-skip-gram-explained The above explains how a static embedding model worksReply·Article·May 25·Retrieval-Augmented Generation (RAG): Chunk, Embed, Retrieve Explained
SPThank youReply·Article·May 25·Retrieval-Augmented Generation (RAG): Chunk, Embed, Retrieve Explained
