Exuding Excellence in AI, Tech Leader Ankush Sharma Makes Waves by Mitigating AI Hallucinations.

One cannot deny how artificial intelligence (AI) has become an inevitable part of the everyday life of almost everyone around the world in some way or another. From drafting medical records to answering customer queries, it is everywhere. However, as these systems spread, a serious challenge arises in the form of AI hallucinations. These can be moments when a model produces fluent, confident answers that can turn out to be wrong. In high-stakes sectors like finance, education, or healthcare, such errors can also cause harm. Among a handful of professionals tackling this challenge efficiently is tech leader Ankush Sharma, a researcher whose name is often closely tied to reliability, observability, and hallucination mitigation in Large Language Models (LLMs).

Ankush Sharma's approach to AI reliability is all about practical innovation. Recently, he garnered headlines for filing a patent titled "Mitigating AI Hallucination in LLM Systems," introducing a consistency-based evaluation framework. At the core of this invention is a consistency-based evaluation framework, allowing AI to check its own answers against external sources and validation layers. He explains how the world needs a systematic approach to reduce hallucinations, and his goal is to make AI outputs auditable, reliable, and safe for real-world applications. He believes that clarity, which ties innovation to accountability, has made him stand apart.

Ankush Sharma's research isn't confined to papers or conferences. In his 2025 project, "Chaos Engineering for LLMs" (Taaleem, 2025), he ensured the adaptation of methods traditionally used to stress-test cloud infrastructure and applied them to AI. This led to the development of new methods for identifying weaknesses and minimizing errors. He points out how healthcare organizations have already benefited. By applying his frameworks to generative AI tools, hospitals have seen improvements in documentation accuracy and patient engagement.

His book, "Observability for Large Language Models: SRE and Chaos Engineering for AI at Scale," has become both an industry and academic reference. Engineers have already cited the book as a playbook for managing live AI systems. On the other hand, professors use it in classrooms to guide the next-gen of computer scientists. The kind of recognition his work has gained is also remarkable. Data science managers report a nearly 40% reduction in production incidents tied to LLM errors after adopting their frameworks. Professors have seen students use his methods for research and projects.

Beyond reliability, Sharma's work on Green AI, which are methods for reducing the environmental footprint of training and deploying models, has influenced sustainability practices worldwide. He has also been inducted into Marquis Who's Who 2025, invited to judge science fairs, and even delivered a TED Talk at Conf42 on LLM observability. Recently, he even joined Cohort 8 of the Climatebase Fellowship, reflecting his conviction that sustainability and reliability must evolve together.

Several experts also believe that Sharma's work will pave the way for a better AI future. Blending resilience, observability, and sustainability, he addresses not only what AI can do but how it should be deployed and responsibly. Industry Leaders have already shared their positive testimonials about Sharma's incredible work.

Utkarsh Mittal (BCS Fellow, Data Science Manager – Gen AI/ML) shared how his book became the single most-referenced artifact on his desk. Rahul Ambuj (Assistant Professor, Department of Computer Science, D.A.V. College) pointed out how the book has proven to be an exceptional contribution to the field and is serving as a highly relevant resource for postgraduate teaching. Sharing his review was also Raj Duggavathi (BVSc, MVSc, PhD, Faculty of Sciences, McGill University), who highlighted that Ankush's book exemplifies a commitment to responsible AI development and has influenced sustainability practices within the tech community.

With degrees from Stanford GSB, MIT, and Southern New Hampshire University, Sharma combines technical expertise with strategic leadership. In doing so, he has not only solved technical flaws but also shaped the ethical foundation of AI's future.