Core Web Vitals in 2025: Diagnosing Performance Bottlenecks with Chrome DevTools

Web performance isn’t just a buzzword – it’s a critical component of user experience and SEO in 2025. Google’s Core Web Vitals (CWV) are at the forefront of this effort, providing metrics that quantify how users perceive your site’s loading speed, interactivity, and visual stability. In essence, CWV help answer: Does your site load fast? Is it responsive quickly? And is it visually stable? If your site lags or shifts unexpectedly, users get frustrated and leave – and search rankings can suffer as a result. This article will take a deep dive into the three key Web Vitals of focus – Largest Contentful Paint (LCP), Cumulative Layout Shift (CLS), and Time to Interactive (TTI) – and how to measure and optimize them using Chrome DevTools and other modern tools. We’ll keep it framework-agnostic and updated for 2025, blending technical insight with approachable guidance for junior devs, seasoned front-end engineers, and even CTOs looking for the big picture.

What are LCP, CLS, and TTI? Largest Contentful Paint measures loading performance – specifically how long it takes for the largest visible element (e.g. hero image or headline) to render on screen. A good LCP is generally 2.5 seconds or less on mobile for the 75th percentile of users. Cumulative Layout Shift gauges visual stability by summing how much page content unexpectedly moves around (layout shifts) during load. A CLS score below 0.1 is considered good, while above 0.25 is poor. Unlike time-based metrics, CLS is unitless – it’s essentially “how bad do things jump around?”. Time to Interactive is a lab metric indicating when the page becomes fully interactive – no long tasks blocking the main thread for a moment, so the page responds swiftly to user input. Generally, a lower TTI (e.g. under about 5 seconds) means the page is usable faster. While TTI isn’t directly measured in the field (it’s computed in lab tests like Lighthouse), it correlates with user experience of responsiveness. Notably, as of 2025 Google has introduced Interaction to Next Paint (INP) as the new field metric for responsiveness, replacing First Input Delay (FID). INP looks at the worst-case interaction delay (not just the first) to better capture real-world responsiveness. However, for diagnosing issues in development, TTI (and its closely related Total Blocking Time metric) remains an important lab indicator of main-thread jank and potential interaction delays. In short, LCP, CLS, and TTI together cover the critical facets of performance: loading, stability, and interactivity. Next, let’s explore how to measure these with Chrome DevTools and complementary tools, and then dive into optimization strategies for each.

Measuring Core Web Vitals in Chrome DevTools

Chrome DevTools is a go-to toolbox for real-time performance diagnosis, and it has evolved significantly by 2025 to help developers focus on Web Vitals. The Performance panel in DevTools can directly display LCP, CLS, and other key timings for a page. By recording a performance trace (open DevTools, go to Performance, start recording and reload or interact with the page), you’ll capture a timeline of events. DevTools marks events like LCP and layout shifts on this timeline in a “Web Vitals” lane, so you can see when the largest content painted and if any layout shifts occurred. For example, after recording, you might see a blue triangle labeled “LCP” at 1.8 s, and yellow layout shift rectangles whenever a shift happens. These visual markers let you pinpoint which resources and actions contributed to LCP and what elements shifted causing CLS. DevTools even integrates field data from the Chrome User Experience Report (CrUX) to show how your local test compares to real-user stats – the live Performance panel can display the page’s typical field LCP/CLS for comparison. This helps you gauge if your environment is significantly faster or slower than what users experience.

Chrome DevTools Performance panel featuring the new Performance Insights sidebar (2025). The timeline is annotated with Core Web Vitals: e.g., LCP occurred at ~0.76 s (local test), CLS accumulated to 0.10. DevTools now compares these with field data (e.g., typical field LCP ~2.4 s) for context. The Insights pane also flags bottlenecks like render-blocking requests and allows drilling into metric details (e.g., breakdown of LCP phases).

DevTools Performance Insights: One of the biggest advancements is the Performance Insights feature. After recording a trace, you can expand the “Performance Insights” sidebar. DevTools will automatically analyze the trace and highlight potential issues – essentially surfacing Lighthouse-like audits in real time. For instance, it might list insights such as “Render-blocking resources”, “Large JavaScript payload”, or “Layout shift culprits” with suggestions. If you click an insight like Render-blocking requests, DevTools will highlight those specific requests in the waterfall, making it clear which CSS or JS files delayed the first render. You can also click on the metric markers themselves. Clicking on the LCP marker, for example, may reveal buttons for “LCP by phase” and “LCP element“. The LCP by phase view is incredibly useful – it breaks down the LCP into sub-timings: time to first byte, resource load delay, resource load time, and element render delay. This tells you where the most time is being spent. For example, LCP might be slow because the resource load delay is high – maybe the image started loading late due to not being prioritized. The breakdown helps target the right fix (e.g., preload the LCP image if there’s a long delay before it starts loading). DevTools can even show the LCP element and which resource was largest, so you know if it was an image, which URL, etc., to optimize.

Similarly, the insights will flag Layout Shift Culprits, identifying which DOM elements moved and contributed most to your CLS. It may point out common causes like “unsized images” (images without width/height) or late-loading web fonts that caused text to reflow. DevTools will highlight those elements in the filmstrip or DOM outline, so you can see what jumped. All of this happens right in the Performance panel – a far cry from having to manually sift through timings.

Apart from Insights, you can of course manually inspect the performance trace. The waterfall chart in DevTools shows when each resource loaded relative to the page start. Look for the LCP marker and see what network request corresponds to it (e.g. a large image). If that image’s request starts late or has a long download, that’s a clue to improve LCP (we’ll discuss how shortly). DevTools also charts Main thread activity – long tasks (over 50ms) are highlighted, which are the enemy of a good TTI. If you see long, blocking script execution during page load, that likely correlates with a high TTI (and high Total Blocking Time). DevTools provides a Bottom-Up and Call Tree view to pinpoint which functions or files took the most time. For example, if a third-party script or a big chunk of your app’s JavaScript spent 300ms doing work before the page became interactive, you’ve identified a TTI bottleneck.

Another helpful DevTools feature is Simulated Throttling. In the Performance panel (or Network conditions), you can throttle CPU and network to emulate slower devices. To realistically diagnose LCP or TTI, you should test under conditions similar to your users e.g. mid-tier mobile on 4G. DevTools uses roughly a Moto G4 profile on “Fast 3G” or “Slow 4G” by default for mobile tests. This ensures your local test produces meaningful LCP/CLS/TTI numbers comparable to field data, and you can see issues that only appear under stress (like images that are fine on desktop but slow on mobile). Tip: DevTools even has a Web Vitals overlay you can enable (in Rendering settings) to display LCP, CLS, etc., as you interact with the page – a quick heads-up display to monitor changes without a full recording.

In summary, Chrome DevTools in 2025 serves as a one-stop lab for Core Web Vitals: use the Performance recording to capture and inspect LCP timing, layout shift events, and main thread blocking. Leverage the Insights suggestions to quickly zero in on problem areas (like heavy scripts or unsized media). And use its built-in comparisons to field data (CrUX) to keep perspective on real-user performance. Once you’ve identified a performance bottleneck in DevTools, you can iterate – make a code change, reload while recording, and see in the live metrics if LCP improved or CLS went down. This tight feedback loop is invaluable for tuning performance in development.

Lighthouse: Automated Auditing and Lab Scores

While DevTools gives you a granular view and interactive diagnosis, Lighthouse provides an automated audit of your page’s performance (among other categories). Lighthouse is integrated into Chrome (the “Lighthouse” tab in DevTools) and is also available via CLI or as part of tools like PageSpeed Insights. When you run a Lighthouse audit (e.g. for Performance), it will load your page in a controlled environment and compute metrics including FCP, Speed Index, LCP, TTI, Total Blocking Time, and CLS, then score your page out of 100. The score is a weighted combination of these metrics. More importantly, Lighthouse outputs a list of opportunities and diagnostics – suggestions to improve the metrics. For example, it might recommend “Eliminate render-blocking resources” if it detected CSS/JS holding up the page, or “Preload key requests” if an important asset like the hero image could be preloaded.

Running Lighthouse in DevTools is straightforward: open the Lighthouse panel, select mobile or desktop and the categories (Performance, etc.), and click Generate report. Within seconds, you get a report with your Core Web Vitals metrics and a “Performance score”. Each metric is labeled good/needs improvement/poor based on thresholds (2.5s for LCP, 0.1 for CLS, etc.). One advantage of Lighthouse is that it simulates a consistent environment – by default mobile Lighthouse runs on a Moto G class device with slow 4G. This makes it easy to track improvements: if you optimize something and the Lighthouse LCP time drops from, say, 4s to 2s, you know you’re on the right track. Keep in mind real users might differ, but Lighthouse is great for A/B testing changes in a lab.

Beyond metrics, pay attention to the Opportunity section. Lighthouse might say, for example, that eliminating unused CSS could save 200ms or reducing JavaScript bundle size could improve TTI by X ms. These suggestions are prioritized by estimated impact on the performance score. Many of them tie directly to Core Web Vitals: e.g., a suggestion to “Preload Largest Contentful Paint image” or “Compress images” is directly aimed at improving LCP. A suggestion to use font-display: swap or preload fonts might be aimed at preventing layout shifts (CLS) due to late-loading fonts. Take these as a to-do list for optimization (we’ll cover specific best practices in the next section).

It’s worth noting that Lighthouse in 2025 has evolved to include the same Insights we saw in DevTools. Google is unifying the advice between Lighthouse and DevTools, calling these “Insights audits”. In fact, PageSpeed Insights now has a “Try Insights” toggle to see the new insight-style recommendations instead of the older list of audits. In Chrome Canary (bleeding-edge), you can even toggle “experimental insights” in the Lighthouse panel to get the new format. These insights highlight things like Layout shift culprits, LCP phases, network dependency chains, etc., similar to DevTools Performance Insights. For example, Lighthouse might explicitly call out “Layout shift culprits: unsized image elements” if it finds images without dimensions causing CLS. Or it might show an LCP resource breakdown so you can see if most of your LCP time was spent waiting for server response vs. downloading the image vs. rendering. This is cutting-edge as of 2025, but it shows the direction: more actionable guidance directly tied to Core Web Vitals.

In practice, you can use Lighthouse both during development and in continuous integration. Many teams run Lighthouse CI to track performance regressions. It’s an easy way to enforce budgets (e.g., fail the build if performance score drops or if LCP exceeds 3s). And since Lighthouse outputs are standardized, you can compare your site against competitors or track improvement over time. Just remember: Lighthouse provides lab data – great for debugging and optimization – but always correlate with real user data. A perfect Lighthouse score is nice (who doesn’t love 100!), but the goal is real users having fast, smooth experiences. So use Lighthouse to catch and fix issues in a test scenario, then verify the impact in the field (using RUM or CrUX).

WebPageTest: Deep-Dive Performance Analysis

For a more advanced, granular look at performance – beyond DevTools and Lighthouse – WebPageTest (WPT) is an invaluable tool. WebPageTest lets you run a page load test on actual browsers (Chrome, etc.) from various locations and network speeds, with a rich array of data. Think of it as a supercharged synthetic testing platform. What makes WPT particularly useful for Core Web Vitals is the level of detail: you get a waterfall chart, filmstrip view, timing breakdowns, and even video capture of the page load. WPT explicitly measures the Core Web Vitals in its test results and highlights them for you.

When you run a test on webpagetest.org, the results page will show your LCP, TBT (lab proxy for interactivity), and CLS right at the top, color-coded (green/yellow/red) against the “good” thresholds. For example, you might see “LCP: 3.2s (Needs Improvement)” in orange, or “CLS: 0.05 (Good)” in green. This gives a quick verdict. But the power of WPT is digging deeper why those numbers are what they are. The filmstrip view is especially enlightening for LCP and CLS. WPT captures screenshots of the page at small intervals (e.g. every 0.5s) during load. In the filmstrip, the moment where the Largest Contentful Paint occurred is marked with a red border around the screenshot. This visually indicates “this is the frame where LCP happened”. You can scan the frames to see what the page looked like just before LCP and at LCP – is the main image appearing at that moment? Does it coincide with some late content loading? The filmstrip also uses yellow borders to indicate any change (something changed from the previous frame), which helps identify where layout shifts might be happening. In fact, WPT has a checkbox “Highlight Layout Shifts” which will overlay a translucent red highlight on the portion of the screenshot that moved on that frame. This is fantastic for visualizing CLS: you can literally see what moved where when a layout shift occurs. For example, you might find that at 2.0s, an ad banner loaded and pushed content down (the highlight will show a red rectangle where the gap change happened).

WebPageTest filmstrip example highlighting the moment of Largest Contentful Paint. The frame with the red border indicates when the LCP element rendered on screen. In this example, an image in a news article is the LCP element (it appears fully at the red-bordered frame). The filmstrip helps verify what content was the largest and when it finished loading.

Below the filmstrip, WebPageTest provides the detailed waterfall chart of all network requests. This is similar to DevTools’ waterfall but often easier to analyze for high-level insights. WPT may overlay vertical lines for events like DOMContentLoaded, First Paint, LCP, etc., on the waterfall. To diagnose LCP, find the request corresponding to your LCP element. For instance, if the LCP element was an image, look for that image’s URL in the waterfall. Check its start time and end time relative to the LCP timestamp. Is there a gap before it starts (which might indicate a late start due to low priority or late discovery)? Is the download time long (indicating maybe a large file size or slow server)? WPT’s waterfall can expose issues like redirects or bad ordering. For example, you might notice the LCP image didn’t begin downloading until after a large JS bundle finished – a sign that perhaps the image loading was unintentionally delayed by a script (e.g., render-blocking script or not using in HTML directly). Using WPT, you could catch that and then decide to preload that image or defer the script.

WebPageTest also provides a metric breakdown graphs. You can see a graph of how CLS accumulated over time, or how the Speed Index (visual completeness) progressed. For CLS, there’s often a timeline graph showing each layout shift contribution. If you have multiple shifts, this can help identify if most of the CLS came from one big shift or many small ones (e.g., a single advertisement injected vs. lots of font swaps).

Another powerful aspect: WPT allows scripting and repeat testing. You can run multiple runs to get median values (mitigating variability). You can also script interactive steps (for example, WPT’s Lighthouse user flows or its own scripting) to measure beyond just the initial load – though measuring post-load INP or CLS with interactions is more advanced and often not needed unless you suspect issues on interaction.

Finally, WPT, being run in the cloud, can simulate various real-world conditions easily – various device types, browser versions, and network speeds. This is helpful to test, say, a high-end desktop on broadband vs a low-end mobile on 3G, to see how Vitals differ. You might find CLS is fine on desktop (fast loading, fewer shifts) but on slow mobile the delayed load of some element causes a late shift. Or LCP might be acceptable on desktop but not on a throttled connection. These insights inform you where to focus optimization (maybe mobile needs special attention like smaller images or avoiding certain content).

In short, WebPageTest complements DevTools and Lighthouse by providing an external, detailed perspective. It excels at visualizing the loading experience (via filmstrip & video) and giving diagnostic information like connection details or HTTP headers that could affect performance. Use WPT when you need to validate improvements (e.g., “did my LCP improve under real network conditions?”) or to diagnose complex cases (like a cluster of shifts or third-party content issues). And the best part: it’s free for basic usage, so there’s no excuse not to test your critical pages! After using DevTools, a run on WPT can confirm your fixes and ensure no environment-specific issues were missed.

Real User Monitoring (RUM) for Field Data

Lab tests are crucial for debugging, but nothing beats actual field data to understand what your users experience. Real User Monitoring (RUM) involves collecting performance metrics from real page loads by real users in production. This is how you truly verify that your LCP is <2.5s for 75% of users, or that CLS issues are solved across all kinds of devices and content. In 2025, setting up RUM for Core Web Vitals has become easier with standard tools and APIs.

First, be aware that Google’s Chrome User Experience Report (CrUX) provides public field data aggregated for sites. Tools like Google PageSpeed Insights and Search Console tap into CrUX to show you, for example, your origin’s 28-day rolling performance – what percentage of visits are “Good” for LCP, FID/INP, CLS. In PageSpeed Insights, the top of the report shows “Field Data” with exactly those stats. But for your own analysis, you’ll likely want more real-time and granular data via a custom RUM setup or a third-party RUM service.

One popular approach is using the Web Vitals JavaScript library that Google provides. It’s a lightweight (~2KB) library that polyfills the logic to capture LCP, CLS, FID (and newer INP, etc.) in the field and gives you callbacks with the values. This abstracts away the complexity of the PerformanceObserver APIs and the nuances of calculating these metrics in the browser. You can simply include the library and have it report metrics to your backend or analytics. For example, using web-vitals.js, you can do:

 webVitals.onLCP((metric) => { /* send metric.value to analytics */ });
webVitals.onCLS((metric) => { ... });
webVitals.onFID((metric) => { ... });

By wiring these into your telemetry (e.g., send to Google Analytics 4 as custom events, or to a logging endpoint), you collect data on every user. Google Analytics (GA4) in fact has built-in support for Core Web Vitals now (in GA4 you can enable tracking of LCP, FID, CLS as automatically collected events or via the library). Many RUM providers like New Relic, Dynatrace, SpeedCurve, etc., also support ingesting Web Vitals – often you just toggle it on in their config. If you use a dedicated RUM service, ask them about Core Web Vitals monitoring. Chances are they have dashboards ready for LCP/CLS/ INP.

Why is RUM so important? Because lab tests might not capture variability – real users are on diverse devices, networks, and may experience different content (personalized or A/B variants). RUM data often shows a distribution of experiences, not just one number. You might find, for example, median LCP is 1.8s (great) but 10% of users have LCP over 4s due to slow 3G or cache misses. That long-tail can still hurt your overall 75th percentile. By examining RUM data, you can identify outliers or specific conditions where performance tanks. Perhaps users in a certain region (with maybe no CDN nearby) have much slower LCP – that insight could justify adding a CDN node or investigating server response times. Or you may discover that a particular page template has worse CLS than others, indicating an element on that page type causing issues.

Another critical reason for RUM: certain metrics like CLS and INP are best measured in the field. As mentioned earlier, lab tests usually capture CLS only during page load, but CLS in the field accumulates for the entire page session. User interactions, late-loading ads, infinite scroll content – these can all produce layout shifts after load that lab tools (which typically stop measuring after load) miss. RUM will catch those. You might have a low CLS in Lighthouse (e.g. 0.02) but RUM shows a higher 0.15 on some pages because users scroll and trigger loading of more images without reserved space. RUM data helps you catch those real-world layout shifts and fix them (perhaps by reserving space for images that load on scroll, etc.). Similarly, for interaction latency, lab tools use Total Blocking Time as a proxy, but only real user interactions can truly measure responsiveness beyond the first input. With INP now a key metric, RUM is the way to measure it – the web-vitals library can capture INP (which looks at all interactions in the first 5s or full page lifetime). If your site has a high INP in RUM data, it flags that some interactions (maybe a slow dropdown or a heavy JS on button click) are causing bad delays. That’s intel you’d never get from a static lab test with no interactions.

A note on thresholds and interpretation: Google evaluates your CWV by looking at the 75th percentile of page visits – if that 75th percentile is within the “good” range for all metrics, the page is considered passing. So when analyzing RUM, focus on the upper percentiles (the slower users). Aim to get at least 75% of users with LCP ≤ 2.5s, CLS ≤ 0.1, INP (or old FID) ≤ 100ms. The RUM data will often show % of good/needs improvement/poor – for example, “78% of users experience good LCP” – which is exactly how Google Search Console reports it. Your goal is to push that number above 75% consistently.

Finally, RUM isn’t just for devs – it can be a company-wide metric. Surface these numbers to product managers and execs. Show how an improvement (say deploying an image optimization) moved the needle from 60% → 80% good LCP in the last month. This helps get buy-in for performance work.

Google’s Search Console CWV report is a great high-level view (red/yellow/green URLs), but your own RUM dashboard can be real-time and more detailed. There are even pre-built dashboards like CrUX Dashboard (in Data Studio) to visualize field data over time. But building your own or using a service like SpeedCurve’s RUM ensures you can segment by page type, device, geo, etc. – slicing the data to find patterns.

In summary: Use RUM to validate that your fixes in lab actually benefit users, and to detect issues that only show up at scale or over time. It completes the feedback loop. A sensible workflow is: use DevTools/Lighthouse/WebPageTest to identify and fix performance bottlenecks in a staging environment, then monitor RUM in production to ensure LCP/CLS/INP improve and stay healthy. If RUM flags a regression (say a deploy caused CLS to spike), you can then go back to the lab tools to diagnose why (maybe a new component injecting content without proper spacing, etc.). This synergy between lab and field data is how you continuously deliver fast, smooth experiences.

Best Practices for Optimizing LCP, CLS, and TTI

Having covered measurement, let’s turn to optimization techniques. Below we outline updated (2025) best practices to improve each Core Web Vital. These recommendations are framework-agnostic – whether you use plain HTML or React/Angular/Vue, the principles hold. The key theme is to deliver content efficiently, avoid surprises in layout, and keep scripts light and non-blocking.

Optimizing Largest Contentful Paint (LCP)

LCP is all about making sure the main content of the page loads fast. You want the user’s eyes to see something meaningful (usually the hero section) quickly. Here are strategies to achieve a fast LCP:

• Improve Server Response Time (TTFB): The very first step in the LCP chain is getting the initial HTML. If your server is slow, everything is delayed. Use caching, CDN edge servers, and optimized back-end code to serve the initial page faster. Aim for an HTML TTFB well under ~800ms (on mobile networks). A fast TTFB means the browser can start loading resources sooner. Tools like Chrome DevTools or Lighthouse will show Time to First Byte explicitly – if it’s a large chunk of your LCP, invest in server optimizations (e.g., query optimization, route caching, etc.).
• Prioritize the LCP Resource: Identify what element is your LCP (DevTools or Lighthouse will tell you). Often it’s an image or video, or sometimes a large text block. Once identified, make sure nothing delays that element’s load. Do not lazy-load your LCP image – lazy-loading defers loading until scroll or other conditions, which is too late for something meant to appear in the initial viewport. “Never lazy-load your LCP image, as that will always lead to unnecessary resource load delay and will have a negative impact on LCP.” Load it immediately. In fact, consider using a preload for the LCP image: a in the head can hint to the browser to fetch it ASAP. This eliminates idle time between the HTML and the start of that image request. In 2025, browsers also support the fetchpriority attribute on images – set fetchpriority="high" on your crucial hero image to signal its importance. By default, images are loaded with lower priority (and can even be delayed by the browser), but marking it high priority ensures the browser doesn’t hold it back behind other resources. Be cautious not to overuse this, but for the one key image above-the-fold, it’s a great boost.
• Eliminate Resource Load Delay: This refers to the gap between when your HTML is parsed and when the LCP resource actually starts loading. One common cause of delay is render-blocking CSS/JS in the head that prevents the browser from discovering the image until late. Inline critical CSS so that external CSS doesn’t block the page from starting render of content. Move non-critical scripts to the end or mark them defer / async so they don’t block HTML parsing. The goal is that as soon as the browser gets the HTML and encounters the for LCP, it should start the fetch without waiting on other head tags. DevTools “LCP subparts” can show if you have a long Resource Load Delay, meaning the LCP image didn’t start until late. The fix might be adding a preload or removing render blockers as above.
• Optimize the LCP Asset: Ensure the LCP image (or video or font) is as optimized as possible. This includes:
  • Using modern image formats (AVIF, WebP) which can significantly reduce file size for the same quality.
  • Compressing the image appropriately (don’t ship a 5000px image when it’s displayed at 1200px; use srcset and size attributes to provide a properly sized image for the viewport). In other words, use responsive images so mobile gets a smaller file than desktop. This not only improves LCP load time but saves user data.
  • Set explicit width and height (or CSS aspect-ratio) on the image to reserve space (this is actually a CLS concern, but it also can help painting go smoother by avoiding re-layout).
  • If the LCP element is text, optimize the font delivery: preload the font or use a system font to avoid delays (more on fonts in CLS section).
• Preload Critical Assets (Fonts, CSS): Sometimes LCP isn’t an image but a large block of text or a banner. If it’s text, the content might be ready but the webfont is slow, delaying rendering. Preload your key web font to speed up First Text Paint. If it’s background image via CSS, consider preloading that image or inlining a small CSS with that background to avoid waiting for a large CSS file. The idea is to treat anything contributing to above-the-fold content as high priority.
• Minimize Main Thread Work Before LCP: If heavy JavaScript is running early, it can delay rendering of the LCP element (especially if the element is rendered by JS). For example, a big framework bundle must load and execute before rendering the content – that can push LCP out by seconds. Strategies like code-splitting can help: don’t load all JS upfront, only load what’s needed to render the initial content. If using React/Vue, consider server-side rendering or hydration strategies that prioritize visible content. If the LCP element is only shown after some script logic (like after an AJAX call), see if that can be made faster or done progressively. DevTools Element Render Delay subpart tells you if after the resource was loaded, there was additional delay before rendering. A common culprit is hidden content (maybe a spinner showing) and then revealed – as shown in the web.dev example where compressing an image didn’t help because the page kept content hidden until JS finished. Avoid such patterns; try to show content as soon as it’s ready.
• Content Delivery Network (CDN): Use a CDN for static resources. Your LCP image and other assets should be delivered from servers geographically close to users. This reduces latency significantly and often improves TTFB and download speeds. In 2025, HTTP/3+QUIC is widely adopted – using CDNs that support HTTP/3 can also shave some time with faster connection setup.

In summary, for LCP: make it your top priority to get that main content in front of the user quickly. That means prioritizing it in load order, slimming it down if possible, and removing any blockers in its way. A quick checklist for LCP could be: Is my server fast? Is my HTML lightweight? Did I mark my hero image as high priority (no lazy, maybe preload)? Did I inline critical above-fold CSS? Is the image compressed and properly sized? Are there any scripts or styles delaying rendering? Address those and you’ll likely see LCP drop dramatically. Remember, a good LCP ≤ 2.5s is the target, but the faster the better (many sites now strive for <2.0s LCP to exceed user expectations).

Optimizing Cumulative Layout Shift (CLS)

CLS issues can be maddening for users – it’s that jarring jump when content moves around unexpectedly. Our goal is to stabilize the layout, ensuring elements don’t shift unless absolutely necessary (and if they do, ideally only in response to user action, which doesn’t count towards CLS if done within 500ms of the interaction). Key practices to minimize CLS:

• Always include size attributes for images and video: This is rule number one. By giving an image a defined width and height (or using CSS to set its container size/aspect ratio), the browser can allocate the correct space before the image loads. That way, when the image loads, it just fills the reserved space instead of pushing content around. In the past, devs omitted width/height for responsive design, but modern browsers preserve aspect ratio from these attributes even if you override actual display size via CSS. For example, if an image is 800×600, include those attrs; you can still make it responsive with max-width:100%, the browser will compute the correct aspect box (800/600) until it has the real image. If your layout uses CSS aspect-ratio boxes (e.g., for fluid embeds), that’s also fine – just ensure some space is reserved. Images,