How to Solve the Biggest Problem with AI

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Prompts:
https://skillleap.futurepedia.io/pages/anti-hallucination-prompts

Links:
NotebookLM - https://notebooklm.google.com/
ChatHub - https://app.chathub.gg/
LLM Council - https://github.com/karpathy/llm-council

Papers mentioned:
AI Survey - https://www.searchlightinstitute.org/wp-content/uploads/2025/12/Searchlight-AI-Survey-Toplines.pdf
A Comprehensive Survey of Hallucination Mitigation Techniques - https://arxiv.org/abs/2401.01313
Instructing LLMs to say 'I Don't Know' - https://arxiv.org/abs/2311.09677
Chain-of-Verification Reduces Hallucinations in LLMs - https://arxiv.org/abs/2309.11495
Chain-of-Thought Prompting Obscures Hallucinations in LLMs - https://arxiv.org/abs/2506.17088
Self-Consistency Improves Chain of Thought Reasoning in LLMs - https://arxiv.org/abs/2203.11171

Summary:
ChatGPT, Gemini, Claude, DeepSeek and Grok all lie to you. It's called hallucinations: where models present false information as fact. I cover proven methods for reducing hallucinations, drawing from recent AI research and focusing on improving overall AI accuracy for large language models. This includes techniques like Retrieval Augmented Generation using NotebookLM, chain of verification, LLM council, prompting techniques, and more.

Chapters
0:00 Intro
0:41 Hallucination example
1:15 The Undisputed Champ (RAG)
2:20 NotebookLM
5:58 Grounding with search
6:20 Anti-hallucination prompt tips
8:19 Better then chain-of-thought
9:12 Chain of verification
11:43 Where these techniques fail
12:44 The Auditor
13:38 Self-consistency
15:47 LLM Council
17:17 Combine methods
18:11 Futurepedia