AI Hallucinations: Why They Happen and How to Control Them
Why AI models produce confident-sounding wrong answers, and practical ways to catch and reduce it.
Not scheduled yet — no date, time or fee fixed. The most-voted topic is hosted next.
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Suggested mentor
Ajay Prajapat
AI Systems Architect
15+ years of experience · Ex-Apple
About mentor & skills Visit siteinnovatewithajay.com
About the suggested mentor
Ajay Prajapat
AI Systems Architect
- 15+ years of experience
- Ex-Apple
AI Systems Architect with 15+ years of experience, including building production GenAI systems — RAG pipelines, AI agents, prompt engineering and GenAI architecture. His sessions are hands-on: expect real workflows, live builds and honest trade-offs, not slideware.
Skills & expertise
- AI-assisted Development
- Full-stack Development
- AI Agents
- RAG & MCP
- System Design
- Project Reviews
- Prompt Engineering
- GenAI Architecture
A suggested match for this topic. The trainer and session details will be confirmed when the webinar is scheduled.
Learn this topic with a mentor
Don't want to wait for the webinar? Pick how you'd like help. Nothing is paid now — you see the fee before anything is booked.
- Bring your own work — code, campaign, report or plan
- Mentor matched to this topic
- Time, format and fee confirmed by email first
What this session would cover
Proposed outline — the mentor finalises the agenda once this topic is scheduled.
- 1What hallucinations are and why LLMs produce them
- 2Situations where hallucinations are more likely
- 3Reducing them with context, sources and constraints
- 4Asking models to admit uncertainty
- 5Detecting hallucinations through verification
- 6Designing applications that limit their impact
Who it's for
- • AI users and developers
- • Professionals relying on AI output
You'd leave with
- An understanding of hallucination causes
- Reduction techniques
- A detection checklist
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