For interpreting crawl reports to plan canonical tags for faceted navigation, a hybrid approach with pre-processing is generally more effective than feeding raw data directly to ChatGPT. Raw crawl data can be vast and complex, making it challenging for the AI to identify subtle patterns without structured input. Instead, first filter and categorize crawl data to highlight common issues like duplicate content, pagination structures, and URL parameters affecting canonicalization. Then, present these summarized findings and specific questions to ChatGPT to leverage its natural language understanding for strategic canonical tag recommendations. This method allows ChatGPT to focus on interpreting identified SEO problems rather than sifting through irrelevant noise or being overwhelmed by sheer volume. It ensures more accurate and actionable insights for creating robust canonicalization strategies across complex faceted navigation. More details: https://blogdelagua.com/adserver/www/delivery/ck.php?ct=1&oaparams=2__bannerid=35__zoneid=8__cb=1de6797466__oadest=https://infoguide.com.ua