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Essential Checklist for Web Optimization

In this competitive technological era, industries have bloomed in a way that we now have a vast array of options to pick from whether we need to shop online or use any services. Therefore, it becomes imperative for businesses to monitor users’ changing demands and keep a close watch on how well their website or app is performing. 

Web/app loading-related metrics:

Imagine going to a website to avail of a service or to shop online, and the website struggles to load every time you select something, let’s say it’s more than 30 seconds. It disrupts the user’s seamless buying experience and eventually causes them to lose interest in just a few minutes. 

Search engines consider factors like page load time, responsiveness, mobile friendliness, etc. when ranking websites. It is necessary to do a complete website analysis and perform a site crawl to comprehend its structure, URL patterns, and template.

Here are a few vital parameters that can help you to do a self-check and understand how easy is your website/app to use for your customers and how you can optimize it for better results. 

Largest Colorful Paint (LCP):

LCP denotes a point when the website’s main content is likely loaded in the page load timeline, making it a crucial user-centric statistic for gauging perceived load speed. A fast LCP tells the user that the page is useful. LCP also indicates the render time of the largest picture or text block visible within the viewport. Sites with 2.5 seconds or less LCP have a good user experience. 

The site has opened but images are not displayed – Bad LCP performance

 Website failed to load and crashes

First Contentful Paint (FCP) – 

FCP is the time taken by the app or website to load the largest and first contentful page. It calculates the duration from when the page begins to load to the time when any page’s content is displayed on the screen.

For this metric, “content” refers to text, images (including background images), <svg> elements, or non-white <canvas> elements.

FCP TimeColor Coding 
0-1.8Green(Fast)
1.8 – 3Yellow (Moderate)
Over 3Red (Slow)

Total Blocking Time (TBT) – The time interval between FCP and TTI 

TBT calculates the total period of time that a website has been blocked from responding to user input. When a task takes more than 50 milliseconds (ms) (which is known as long task) to complete on the main thread, the main thread gets blocked and the browser cannot stop an ongoing task. Therefore, if a user does interact with the page in the middle of a long task, the browser will have to wait until the task is finished before responding. The user is likely to notice the delay and consider the page to be unresponsive. Ex: When a video ad pops up.

Total Time to Interactive (TTI) – Time taken by the website/app to get ready to collect inputs from the user (Username, password, etc).

TTI estimates the amount of time it takes for a page to load from the time it first loads until its primary sub-resources have loaded.

Techniques like server-side rendering (SSR) may result in situations where a page appears interactive (that is, links and buttons are visible on the screen) but it’s not interactive as the main thread is blocked or the JavaScript code controlling those elements hasn’t been loaded.

Cumulative Layout Shift (CLS) 

A webpage’s CLS tells you how much it suddenly shifts throughout the course of its existence. A high CLS score is achieved if a website visitor viewed a page and, as they were reading it, the banner loads and the page jumps down.

CLS is a component of Google’s Core Web Vitals, along with Largest Contentful Paint and First Input Delay (how long it takes for a website to be interactive or “clickable”). Each page that Google web crawlers index has a CLS measurement.

Tips to optimize your website’s load time:

  • Anticipate your user traffic

Predict when there might be a spike and how much can be the maximum traffic you can expect – for instance, days like sales, special offers, etc.

  • Understand and study the Consumer behavior 

Narrow down and find out what are the user patterns – peak traffic achieved during which time intervals, which is the most visited section, and how much time a user spends on the website or app. Ensure the most visited sections perform well always and be extra cautious during peak times.

  • Ask and ask always

 Product/ Service feedback is pivotal when planning where to optimize and how to achieve the best results for user engagement, satisfaction, and retention. Surveys are an evergreen and classic way to do a self-assessment.

  • Keep track and scale up when needed

Track the above-mentioned data from time to time, document it, and do a detailed data analysis. Do a regular check and scale up as your user base increases. Remember, the performance of a website might be good for thousands of users but might need to improve when the user base expands to a lakh.

  • Competitive Analysis

Take inspiration from competitors in your field. Study how many users they have, how seamless their site features are, and how time efficient they are.

  • Introduce new features to save the users time like image optimization, and optimizing your Javascript codes which will help you to improve the user experience.

To engage your users for a longer time, always save their time and effort :) 

We’ll discuss web optimization techniques in the next blog. Stay Tuned!

Further reading: Why Web Optimization is a Must for Businesses?

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Machines That Make Up Facts? Stopping AI Hallucinations with Reliable Systems

There was a time when people truly believed that humans only used 10% of their brains, so much so that it fueled Hollywood Movies and self-help personas promising untapped genius. The truth? Neuroscientists have long debunked this myth, proving that nearly all parts of our brain are active, even when we’re at rest. Now, imagine AI doing the same, providing information that is untrue, except unlike us, it doesn’t have a moment of self-doubt. That’s the bizarre and sometimes dangerous world of AI hallucinations.

AI hallucinations aren’t just funny errors; they’re a real and growing issue in AI-generated misinformation. So why do they happen, and how do we build reliable AI systems that don’t confidently mislead us? Let’s dive in.

Why Do AI Hallucinations Happen?

AI hallucinations happen when models generate errors due to incomplete, biased, or conflicting data. Other reasons include:

  • Human oversight: AI mirrors human biases and errors in training data, leading to AI’s false information
  • Lack of reasoning: Unlike humans, AI doesn’t “think” critically—it generates predictions based on patterns.

But beyond these, what if AI is too creative for its own good?

‘Creativity Gone Rogue’: When AI’s Imagination Runs Wild

AI doesn’t dream, but sometimes it gets ‘too creative’—spinning plausible-sounding stories that are basically AI-generated fake data with zero factual basis. Take the case of Meta’s Galactica, an AI model designed to generate scientific papers. It confidently fabricated entire studies with fake references, leading Meta to shut it down in three days.

This raises the question: Should AI be designed to be ‘less creative’ when AI trustworthiness matters?

The Overconfidence Problem

Ever heard the phrase, “Be confident, but not overconfident”? AI definitely hasn’t.

AI hallucinations happen because AI lacks self-doubt. When it doesn’t know something, it doesn’t hesitate—it just generates the most statistically probable answer. In one bizarre case, ChatGPT falsely accused a law professor of sexual harassment and even cited fake legal documents as proof.

Take the now-infamous case of Google’s Bard, which confidently claimed that the James Webb Space Telescope took the first-ever image of an exoplanet, a factually incorrect statement that went viral before Google had to step in and correct it.

There are more such multiple instances where AI hallucinations have led to Human hallucinations. Here are a few instances we faced.

When we tried the prompt of “Padmavaat according to the description of Malik Muhammad Jayasi-the writer ”

When we tried the prompt of “monkey to man evolution”

Now, if this is making you question your AI’s ability to get things right, then you should probably start looking have a checklist to check if your AI is reliable.

Before diving into solutions. Question your AI. If it can do these, maybe these will solve a bit of issues:

  • Can AI recognize its own mistakes?
  • What would “self-awareness” look like in AI without consciousness?
  • Are there techniques to make AI second-guess itself?
  • Can AI “consult an expert” before answering?

That might be just a checklist, but here are the strategies that make AI more reliable:

Strategies for Building Reliable AI

1. Neurosymbolic AI

It is a hybrid approach combining symbolic reasoning (logical rules) with deep learning to improve factual accuracy. IBM is pioneering this approach to build trustworthy AI systems that reason more like humans. For example, RAAPID’s solutions utilize this approach to transform clinical data into compliant, profitable risk adjustment, improving contextual understanding and reducing misdiagnoses.

2. Human-in-the-Loop Verification

Instead of random checks, AI can be trained to request human validation in critical areas. Companies like OpenAI and Google DeepMind are implementing real-time feedback loops where AI flags uncertain responses for review. A notable AI hallucination prevention use case is in medical AI, where human radiologists verify AI-detected anomalies in scans, improving diagnostic accuracy.

3. Truth Scoring Mechanism

IBM’s FactSheets AI assigns credibility scores to AI-generated content, ensuring more fact-based responses. This approach is already being used in financial risk assessment models, where AI outputs are ranked by reliability before human analysts review them.

4. AI ‘Memory’ for Context Awareness

Retrieval-Augmented Generation (RAG) allows AI to access verified sources before responding. This method is already being used by platforms like Bing AI, which cites sources instead of generating standalone answers. In legal tech, RAG-based models ensure AI-generated contracts reference actual legal precedents, reducing AI accuracy problems.

5. Red Teaming & Adversarial Testing

Companies like OpenAI and Google regularly use “red teaming”—pitting AI against expert testers who try to break its logic and expose weaknesses. This helps fine-tune AI models before public release. A practical AI reliability example is cybersecurity AI, where red teams simulate hacking attempts to uncover vulnerabilities before systems go live 

The Future: AI That Knows When to Say, “I Don’t Know”

One of the most important steps toward reliable AI is training models to recognize uncertainty. Instead of making up answers, AI should be able to respond with “I’m unsure” or direct users to validated sources. Google DeepMind’s Socratic AI model is experimenting with ways to embed self-doubt into AI.

Conclusion:

AI hallucinations aren’t just quirky mistakes—they’re a major roadblock in creating trustworthy AI systems. By blending techniques like neurosymbolic AI, human-in-the-loop verification, and retrieval-augmented generation, we can push AI toward greater accuracy and reliability.

But here’s the big question: Should AI always strive to be 100% factual, or does some level of ‘creative hallucination’ have its place? After all, some of the best innovations come from thinking outside the box—even if that box is built from AI-generated data and machine learning algorithms.

At Mantra Labs, we specialize in data-driven AI solutions designed to minimize hallucinations and maximize trust. Whether you’re developing AI-powered products or enhancing decision-making with machine learning, our expertise ensures your models provide accurate information, making life easier for humans

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