As AI-driven content pipelines evolve, evaluating models that transform keywords into blogs and further into podcast scripts requires a multi-dimensional approach. Key criteria include keyword relevance (semantic coverage and contextual usage), content quality in blogs (readability, coherence, originality, and SEO optimization), and adaptation quality for podcasts (conversational tone, engagement, and audience-friendliness). Additionally, ensuring semantic consistency across all stages, preserving factual accuracy, and incorporating human-like delivery are crucial. Evaluation should combine automated metrics (readability scores, embeddings for similarity, sentiment analysis) with AI-based judges (scoring creativity, engagement) and human feedback. The challenge lies in deciding how to weigh these metrics and whether optimization should be stage-specific or holistic, making this an ideal topic for discussion among ML engineers and content professionals.
What are your thoughts?