Compare two prompts as vectors in semantic embedding space. Returns cosine similarity, semantic-shift analysis, shared vs unique concepts, intent match, and a likelihood prediction of whether both prompts produce the same response.
// Grounded in transformer-internals.md (Engineering Analysis 34 + linear-algebra concept 31 sources)Read what your model is actually doing. Attention-head analysis, embedding-space probing, SVD-based LoRA design. When prompt-tuning has plateaued, this is the layer beneath.
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