A decade ago, you’d type something like “recipe+chicken+broccoli+carrots+leftover” into a search engine, hoping for edible inspiration. Today, you simply ask, “What can I make with leftover chicken, half a broccoli, and three sad-looking carrots?” and get a personalized recipe suggestion complete with cooking tips and possible substitutions. This isn’t just a convenient upgrade—it’s a fundamental shift in how we interact with information, powered by artificial intelligence that finally speaks our language.

The Algorithm Paradox

With over 2.5 quintillion bytes of data created daily, human curation alone can’t keep pace. Instead, algorithms handle the massive data processing requirements, while AI provides an intuitive, human-friendly interface. Take Netflix, for instance—their recommendation algorithm processes billions of user interactions to feel as personal as a friend suggesting your next favorite show.

Similarly, In retail, algorithms power visual search tools, allowing users to find products by uploading images. Algorithms also drive applications in healthcare, like symptom checkers, which rely on natural language processing (NLP) algorithms to match patient inputs to medical databases. These intricate systems enable AI to transform raw data into actionable, context-aware insights that define modern search experiences. By combining these algorithmic capabilities with AI’s intuitive interface, search engines are evolving into intelligent systems capable of delivering hyper-relevant results in real-time.

Under the Hood: LLMs and Data Engineering

Large Language Models (LLMs), the polyglots of the digital age. These AI engines process words while understanding context, intent, and subtle nuances. These aren’t just word processors with a fancy upgrade—they’re more like master interpreters who’ve absorbed the collective knowledge of humanity and can connect dots across disciplines at lightning speed. Generative AI, as seen in platforms like ChatGPT, represents a leap forward in this capability, enabling even more dynamic and creative solutions.

The real unsung hero, though, is data engineering. If LLMs are the brain, data engineering is the nervous system, creating highways of information that make split-second insights possible. According to Stanford’s AI Index Report, this combination has revolutionized how we process and understand information, reducing complex query times from hours to milliseconds.

The New Face of Search Engine

Today’s AI search engines don’t just find information; they understand, synthesize, and present it in ways that feel remarkably human. Today’s AI search engines are powered by an impressive arsenal of generative AI technology:

RankBrain: This system excels at interpreting the intent and context behind queries, making search results more relevant and insightful. For example, when someone searches for the “best laptop for graphic design under $1,000,” RankBrain identifies the user’s need for budget-friendly options with specific features and surfaces the most pertinent results.
BERT (Bidirectional Encoder Representations from Transformers): Unlike older algorithms that processed queries word-by-word, BERT considers the entire sentence to understand the context. For instance, a query like “2019 Brazil traveler to USA need a visa” might have been misunderstood by previous systems as a U.S. traveler needing a visa for Brazil. BERT, however, interprets the preposition “to” correctly, recognizing the intent as a Brazilian seeking information about U.S. visa requirements. This nuanced understanding significantly improves search accuracy.
MUM (Multitask Unified Model): MUM goes beyond understanding words; it grasps complex contexts across languages and content formats. Imagine searching, “I’ve hiked Mt. Adams and now want to hike Mt. Fuji next fall, what should I do differently to prepare?” MUM can analyze this query holistically, comparing the two mountains, identifying key differences, and suggesting appropriate preparation steps, such as suitable gear or training tips.

These systems enable transformative capabilities:

Natural language processing has slashed search times by 45% (Stanford Research)
Translation accuracy now reaches 95% for major languages
Personalized results are 34% more relevant than traditional algorithms

Enhancing Internal Search with LLMs

Organizations are transforming how they access and utilize information by integrating Large Language Models (LLMs) into their internal workflows. With innovations like Retrieval Augmented Generation (RAG), LLMs are making internal search capabilities faster, smarter, and more reliable. For instance, companies can now embed LLMs with their proprietary knowledge bases, enabling employees to retrieve precise answers to complex questions instantly. Whether it’s customer service teams resolving issues more efficiently, healthcare professionals accessing clinical protocols and diagnostic guidelines, or engineers finding technical documentation in seconds, LLMs are breaking down information silos across industries. By streamlining access to critical data, businesses empower their teams to make informed decisions faster, collaborate seamlessly, and stay ahead in a rapidly evolving landscape.

Charting the Future with AI Search Engine

As we stand at this transformative junction, AI isn’t just changing how we find information, AI is fundamentally reshaping our digital interactions. The democratization of Artificial intelligence through platforms like OpenAI and others has turned cutting-edge AI capabilities into accessible AI tools for businesses of all sizes.

The accessibility has sparked a revolution. Healthcare professionals can now instantly access life-saving protocols, manufacturers are streamlining operations with predictive maintenance, and even small businesses can offer sophisticated search experiences that rival tech giants. The explosion of open-source AI tools has created a playground where innovation knows no bounds.

At Mantra Labs, we’re at the forefront of this search revolution. Our expertise spans custom-built LLMs and robust data engineering pipelines. Whether enhancing internal knowledge management, improving customer experiences, or building next-gen search applications, we’re here to help turn your vision into reality. Let’s shape the future of search together.

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

By : Sampada S M

Published on Apr 1, 2025

AI AI Hallucinations Artificial Intelligence Reliable AI 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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