Would you trust your AI chatbot to help you build customer trust, develop your restaurant’s next menu, or handle sensitive financial and healthcare information?
Key AI-related incidents that made headlines recently were largely due to AI hallucination, bias, and lack of adequate human oversight, leading to public embarrassment, damage to reputation and, in some cases, financial consequences:
- Deloitte’s hallucinatory government reports caused the consulting firm significant backlash and forced it to issue partial refunds to both the Australian and Canadian governments after submitting official reports that contained numerous AI-generated errors, including fake academic citations and non-existent quotes from public figures.
- Both McDonald’s and Taco Bell scrapped AI voice ordering pilots after viral social media videos showed the systems mistakenly adding hundreds of chicken nuggets to orders or being easily trolled by users who ordered absurd amounts of water cups.
- Elon Musk’s AI chatbot, Grok, drew widespread ridicule recently for repeatedly claiming its creator was the “fittest man alive” (fitter than LeBron James) and smarter than historical geniuses like Einstein and Da Vinci. Musk blamed “adversarial prompting” for the responses, but critics pointed to embedded bias within the system.
- And, in multiple instances across the globe, lawyers have been sanctioned by judges for submitting legal briefs that cited entirely fictional case law and statutes invented by generative AI tools like ChatGPT.
We find out more about the causes of AI failures, the impact, and what organizations should do to safeguard against AI sabotage from Andre Scott, Developer Advocate, Coralogix.
Why do AI chatbots make so many errors?
Scott: There are two fundamental issues.
First, most AI systems lack proper guardrails; they’re essentially powerful tools without safety constraints.
Second, we’ve moved beyond needing prompt engineers to needing ‘AI content engineers’ who understand how to structure system instructions, define operational boundaries, and build in misuse protection. Many companies are still treating AI like traditional software when it requires completely different design principles.
What damage can AI mistakes do to a company’s reputation and bottom line?
Scott: We’ve seen catastrophic examples recently. DPD’s chatbot went viral for writing poetry about how terrible the company was; that’s brand damage you can’t easily recover from. Google’s AI recommended putting glue on pizza.
But beyond viral incidents, there’s silent damage, including PII leakage, incorrect financial advice, or healthcare misinformation. Imagine an AI confidently giving wrong medical guidance or leaking customer data. Traditional monitoring would show ‘everything working’ while business-critical failures happen in real-time. Customer trust, once lost, takes years to rebuild.
Why is it important to monitor not just AI performance, but also its content?
Scott: Traditional observability asks ‘Is it running?’ but AI observability must ask ‘Is it right?’ Your API can return a perfect 200 response while the AI hallucinates completely wrong information.
Most AI computation happens in external models like GPT or Gemini; you’re essentially outsourcing your business logic. You need new metrics: correctness, security violations, cost per interaction, topic adherence, PII exposure. Traditional APM tools weren’t built for this.
That’s why we built evaluation engines — AI systems that monitor AI systems. At Coralogix, our AI Center uses specialized models to evaluate every interaction for quality, security, and business logic compliance in real-time.
Could you tell us more about AI observability and guardrails?
Scott: Guardrails are your defense against the very risks you’re evaluating for. Take code generation: one bad SQL query from an AI can expose your entire database or crash your system. With proper evaluation and guardrails, you prevent these failures before they reach production.
Evaluation is the crown jewel of AI observability. But what’s unique about our approach at Coralogix is that we provide full-stack correlation. If front-end performance is affecting a chatbot, or a vector database is causing latency spikes, we correlate AI metrics with the entire infrastructure stack using OpenTelemetry standards.
