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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 Systems

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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Empowering Frontline Healthcare Sales Teams with Mobile-First Tools

In healthcare, field sales is more than just hitting quotas—it’s about navigating a complex stakeholder ecosystem that spans hospitals, clinics, diagnostics labs, and pharmacies. Reps are expected to juggle compliance, education, and relationship-building—all on the move.

But, traditional systems can’t keep up. 

Only 28% of a rep’s time is spent selling; the rest is lost to administrative tasks, CRM updates, and fragmented workflows.

Salesforce, State of Sales 2024

This is where mobile-first sales apps in healthcare are changing the game—empowering sales teams to work smarter, faster, and more compliantly.

The Real Challenges in Traditional Field Sales

Despite their scale, many healthcare sales teams still rely on outdated tools that drag down performance:

  • Paper-based reporting: Slows down data consolidation and misses real-time insights
  • Siloed CRMs: Fragmented systems lead to broken workflows

According to a study by HubSpot, 32% of reps spend at least an hour per day just entering data into CRMs.

  • Managing Visits: Visits require planning, which may involve a lot of stress since doctors have a busy schedule, making it difficult for sales reps to meet them.
  • Inconsistent feedback loops: Managers struggle to coach and support reps effectively
  • Compliance gaps: Manual processes are audit-heavy and unreliable

These issues don’t just affect productivity—they erode trust, delay decisions, and increase revenue leakage.

What a Mobile-First Sales App in Healthcare Should Deliver

According to Deloitte’s 2025 Global Healthcare Executive Outlook, organizations are prioritizing digital tools to reduce burnout, drive efficiency, and enable real-time collaboration. A mobile-first sales app in healthcare is a critical part of this shift—especially for hybrid field teams dealing with fragmented systems and growing compliance demands.

Core Features of a Mobile-First Sales App in Healthcare

1. Smart Visit Planning & Route Optimization

Field reps can plan high-impact visits, reduce travel time, and log interactions efficiently. Geo-tagged entries ensure field activity transparency.

2. In-App KYC & E-Detailing

According to Viseven, over 60% of HCPs prefer on-demand digital content over live rep interactions, and self-detailing can increase engagement up to 3x compared to traditional methods.
By enabling self-detailing within the mobile app, reps can deliver compliance-approved content, enable interactive, personalized detailing during or after HCP visits, and give HCPs control over when and how they engage.

3. Real-Time Escalation & Commission Tracking

Track escalation tickets and incentive eligibility on the go, reducing back-and-forth and improving rep satisfaction.

4. Centralized Knowledge Hub

Push product updates, training videos, and compliance checklists—directly to reps’ devices. Maintain alignment across distributed teams. 

5. Live Dashboards for Performance Tracking

Sales leaders can view territory-wise performance, rep productivity, and engagement trends instantly, enabling proactive decision-making.

Case in Point: Digitizing Sales for a Leading Pharma Firm

Mantra Labs partnered with a top Indian pharma firm to streamline pharmacy workflows inside their ecosystem. 

The Challenge:

  • Pharmacists were struggling with operational inefficiencies that directly impacted patient care and satisfaction. 
  • Delays in prescription fulfillment were becoming increasingly common due to a lack of real-time inventory visibility and manual processing bottlenecks. 
  • Critical stock-out alerts were either missed or delayed, leading to unavailability of essential medicines when needed. 
  • Additionally, communication gaps between pharmacists and prescribing doctors led to frequent clarifications, rework, and slow turnaround times—affecting both speed and accuracy in dispensing medication. 

These challenges not only disrupted the pharmacy workflow but also created a ripple effect across the wider care delivery ecosystem.

Our Solution:

We designed a custom digital pharmacy module with:

  • Inventory Management: Centralized tracking of sales, purchases, returns, and expiry alerts
  • Revenue Snapshot: Real-time tracking of dues, payments, and cash flow
  • ShortBook Dashboard: Stock views by medicine, distributor, and manufacturer
  • Smart Reporting: Instant downloadable reports for accounts, stock, and sales

Business Impact:

  • 2x faster prescription fulfillment, reducing wait times and improving patient experience
  • 27% reduction in stock-out incidents through real-time alerts and inventory visibility
  • 81% reduction in manual errors, thanks to automation and real-time dashboards
  • Streamlined doctor-pharmacy coordination, leading to fewer clarifications and faster dispensing

Integration Is Key

A mobile-first sales app in healthcare is as strong as the ecosystem it fits into. Mantra Labs ensures seamless integration with:

  • CRM systems for lead and pipeline tracking
  • HRMS for leave, attendance, and performance sync
  • LMS to deliver ongoing training
  • Product Catalogs to support detailing and onboarding

Ready to Empower Your Sales Teams?

From lead capture to conversion, Mantra Labs helps you automate, streamline, and accelerate every step of the sales journey. 

Whether you’re managing field agents, handling complex product configurations, or tracking customer interactions — we bring the tech & domain expertise to cut manual effort and boost productivity.

Let’s simplify your sales workflows. Book a quick call.

Further Reading: How Smarter Sales Apps Are Reinventing the Frontlines of Insurance Distribution

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