If you've ever stared at a Lighthouse report or Google Search Console's Core Web Vitals data and felt overwhelmed by where to start, you're not alone. Page speed optimization is one of those tasks that every developer knows matters, but few have time to tackle systematically. That's exactly the problem a new wave of AI tools — including Junia AI — aims to solve.

Feed It Raw Data, Not Pretty Reports

The biggest mistake developers make when using AI for page speed work is feeding it summary reports or pretty dashboards instead of raw metrics. According to guidance published on DEV.to this week, Junia AI works best when you give it the actual Core Web Vitals data: your Largest Contentful Paint times, Cumulative Layout Shift scores, and First Input Delay numbers in their unprocessed form. The more context you provide upfront, the better the recommendations become — because the AI can actually see what you're seeing rather than interpreting someone else's interpretation.

Prioritize by Impact, Not Alphabetically

Once you've got your data loaded, the next critical step is asking Junia AI to prioritize fixes by impact rather than accepting a flat list of suggestions. This sounds obvious, but it's where most workflows fall apart. A simple rephrase like 'Which three changes would have the biggest effect on my LCP score?' yields dramatically different results than 'Give me all recommendations.' The difference between a 20-minute workflow and a multi-day audit often comes down to asking for prioritization from the start.

The 20-Minute Workflow

The most reliable approach takes roughly 20 minutes end-to-end: export your raw Core Web Vitals data, paste it into Junia AI with a clear request for prioritized recommendations, review the top three suggestions, implement one fix, re-test, and repeat. This iterative approach keeps momentum high and prevents analysis paralysis. You don't need to fix everything at once — you need to fix the right things in the right order.

Beyond Basic Recommendations

What makes Junia AI particularly useful for page speed work is its ability to connect recommendations to your specific site architecture. Unlike generic audit tools that give you the same suggestions regardless of context, an AI with full visibility into your stack can suggest image optimization approaches that actually fit your current pipeline or code-splitting strategies tailored to your framework choices.

Key Takeaways

  • Always feed AI page speed tools raw Core Web Vitals data, not formatted reports
  • Ask for prioritization by impact before reviewing any recommendations
  • The complete workflow takes about 20 minutes when done systematically
  • Iterate: fix one high-impact item, re-test, then move to the next

The Bottom Line

Page speed optimization doesn't have to be a weekend project that never gets finished. By feeding Junia AI raw metrics and asking for impact-based prioritization, you can turn an overwhelming audit into a focused 20-minute session that actually moves your Core Web Vitals numbers. Start with LCP — that's where most sites see the biggest gains.