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A Synopsis of Customer Review and Feedback Systems

Customer experience is a top priority for businesses. While customer feedback is important to improve products/services according to user preferences; reviews play the role of word-of-mouth communication in the digital world. For instance, 86% of millennials admit they’re influenced by negative reviews, according to Dimensional Research.

Organizations have started to invest in customer feedback systems to prove their customer-focused vision. However, the benefits from customer feedback converge to responding to them at the right moment. Let’s take a look at the importance of capturing customer opinion at the right time instead of behindhand traditional methods.

Why Act on Customer Opinions Promptly? 

According to the Bright Local consumer review survey, 44% of customers consider more than a month’s old review as irrelevant. Upgrading the product as per customer feedback in a short time is indeed a challenge.

Traditionally, organizations employ advocacy, legacy, support teams, and community managers to look after customer reviews on different social platforms. Monitoring different platforms and communicating internally to resolve customers’ concerns are tedious and account for a delay in action. This is where the customer feedback system simplifies the process. But before, let’s look at the benefits of addressing customer feedback in this era of the consumer internet.

  • A chance to improve products and services: It’s obvious, brands do thorough market research before finalizing a product. But still, the final product carries some scope of improvement. Apart from beta testing, customer feedback is also a useful resource to improve product post-release. 
  • Delivering value to customers: Customer loyalty is hard-won in the world of over 200 million companies! With empathy and understanding how a customer feels for the product brands can establish an emotional connection with the customer and hence their loyalty.
  • Helps in making a fact-based business decision: The decision to accommodate customer preferences in the existing product or introduce a new product should not be based on loose guesses. Analyzing customer reviews can help organizations make better business decisions.
  • Measure customer experience and satisfaction: Customers are the actual brand advocates. Their experience and satisfaction determine a product’s success. Walker study predicts- by 2020, customer experience will become the key brand differentiator surpassing price and product. In this regard, Zappos’ customer service is an epitome of customer experiences.

Also, read – Customer Journey is the NEW Product.

Customers use different channels to share their opinion about a product or service. Microsoft reports, “66% of consumers have used at least 3 different communication channels to contact customer service.” This indicates that customers want organizations to address their feedback or complaint immediately. 

How Are Organizations Leveraging Customer Feedback and Reviews?

Customer reviews are a reliable source of information about a product/service to potential customers. According to Bright Local, 93% of customers read reviews to determine how good or bad a local business is. Customer reviews are therefore an influential source for branding and marketing. Freethinkers have identified the importance of customer feedback and that’s why we see a growing number of e-commerce marketing platforms. 

For instance, Yotpo provides a one-stop solution for visual marketing, loyalty, and referrals based on customer feedback. Companies can collect customer reviews and share them across different social media channels. They can also build loyalty and referral programs to develop a long-term association with customers.

Similarly, Trustpilot has over 300,000 businesses reviewed by customers. It is providing companies with a platform to leverage customer reviews to influence future buyers. It helps startups to get more reviews, engage with customers, and analyze the results to improve.

Final Thoughts

According to Womply, businesses with a free listing on at least 4 review sites earn 58% more revenue. This indicates that customers easily trust strangers who’re not friends to the company. Organizations can leverage the power of reviews through a customer feedback system to create ‘beyond satisfactory’ experiences for customers. 

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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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