NemoClaw Knowledge Wiki

hallucination

5 min read

Hallucination

A phenomenon where an AI system generates content that is not grounded in its training data or external knowledge sources, often appearing as fabricated or incorrect information.

Key Characteristics

  • Non-factual output: Information presented as factual but unverifiable or false.
  • Confidence mismatch: High confidence in incorrect statements.
  • Contextual disconnect: Responses unrelated to input prompts.

Causes

  • Data gaps: Insufficient or noisy training data.
  • Model limitations: Overfitting or underfitting during training.
  • Prompt engineering: Ambiguous or misleading user queries.

Mitigation Strategies

  • Multi-agent systems: Combining multiple LLMs to cross-validate outputs.
  • External verification: Integrating retrieval-augmented generation (RAG) for fact-checking.
  • Human-in-the-loop: Manual review of critical outputs.

References

New Research

Source Notes

  • 2026-04-14: # Database search setup - Channel Tech with Tim --- --- https://www.youtube.com/watch?v=XEiQV4zRC-U Here is a markdown summary of the video transcript. # The Future of Database Search: pg_ (Database search setup - Channel Tech with Tim)
  • 2026-04-14: # Difference between RAG and Agents for workflow --- --- https://www.youtube.com/watch?v=WYqhc802nqk Here is a detailed breakdown of the video “RAG vs Agents” by Dr. Anil Variyar. Video Summary Dr. Anil Variyar provides a vi (Difference between RAG and Agents for workflow)
  • 2026-04-14: # Discover AI channel - Graph RAG evolved --- --- https://www.youtube.com/watch?v=oetP9uksUwM This video provides a comprehensive overview of the evolution of Retrieval-Augmented Generation (RAG) systems, from foundational RAG to GraphRAG, Light (Discover AI channel - Graph RAG evolved)
  • 2026-04-14: # Enhanced rag. Channel Prompt Engineering --- --- https://youtu.be/xG3eS\_zHR3k?si=YBSLkDwCMRe04C9h Here is a Markdown summary and technical overview of the video content r (Enhanced rag. Channel Prompt Engineering)
  • 2026-04-14: # Fw Replay & Resources 🎥 Building Smarter RAG Systems with Muhammad Farooq --- --- Dr. Paul Cooper, Ph.D, FAIDH, CHIA, AFHEA, GAICD Director, Longboardfella Consulting Pty Ltd www.longboardfella.com ACN: 650 470 474 Ph: +61 487 198 798 paul@longboardf (Fw Replay & Resources 🎥 Building Smarter RAG Systems with Muhammad Farooq)
  • 2026-04-14: # GraphRAG explainer --- --- https://www.youtube.com/watch?v=EUG65dIY-2k This video provides a comprehensive guide on how to enhance the accuracy and reliability of AI agents by integrating knowledge graphs, a technique known as GraphRAG (Retrieval-Augment (GraphRAG explainer)
  • 2026-04-14: # IBM agentic security --- --- https://www.youtube.com/watch?v=wiU7VEvi1LM The video provides an overview of challenges and strategies for secure identity propagation within agentic syste (IBM agentic security)
  • 2026-04-14: # Improving RAG accuracy for retrieval --- --- https://www.youtube.com/watch?v=smGbeghV1JE This video details how a client project successfully improved the recall of its Retrieval-Augmented Generation (RAG) system from 50-60% to over 9 (Improving RAG accuracy for retrieval)
  • 2026-04-14: # Knowledge Graph or Vector Database RAG comparison --- --- https://www.youtube.com/watch?v=6vG_amAshTk Video by Adam Lucek This video provides a detailed introduction to Knowledge Knowledge Graph or Vector Database RAG comparison)
  • 2026-04-14: # Nanonets OCR for tables to text for RAG --- --- https://www.youtube.com/watch?v=j7oxmKCwCPM Of course. Here is a summary of the video. Author: In this video from his AI and machine learning channel, the author provides an in-depth look at a new open-source OCR model. **Su (Nanonets OCR for tables to text for RAG)
  • 2026-04-14: # Prompt Engineering - Local GPT for RAG --- --- https://www.youtube.com/watch?v=JTbtGH3secI This video, titled “Why Your AI Models Are Hallucinating & How to Fix Them,” provides a comprehensive overview of the phenomenon of “hallucination” in Large Language Models (LLMs) (Prompt Engineering - Local GPT for RAG)
  • 2026-04-14: # RAG agentic search - Prompt Engineering channel --- --- https://www.youtube.com/watch?v=QxBJ9ORecMY Here is a Markdown summary of the video transcript, detailing the architecture and functionality of the Hybrid Agentic File Search system. * * * # Agentic File Search vs. (RAG agentic search - Prompt Engineering channel)
  • 2026-04-14: # Using docling and Llamaparse in RAG --- --- https://www.youtube.com/watch?v=eHw_6jhK8AM The video explores how to integrate diverse file formats into AI agents, focusing on Retrieval Augmented Generation (RAG) systems. The presenter introduces three document parsing solu (Using docling and Llamaparse in RAG)
  • 2026-04-14: # What is OpenRAG - IBM Channel --- --- https://youtu.be/qreMmsOY86A Here is a comprehensive Markdown document based on the video presentation by David Jones-Gilardi, Developer Relations Engineer at IBM. * * * # What is OpenRAG? An Overview of Agentic RAG Systems Speaker: D (What is OpenRAG - IBM Channel)
  • 2026-04-07: Chroma Context-1: Self-Editing Search Agent for Efficient RAG Clip title: Next Evolution of Retrieval-Augmented Generation Author / channel: Prompt Engineering URL: https://www.youtube.com/watch?v=7f1bHER4kRM Summary Chroma Context-1 is introduced as a groundbr (Chroma Context-1: Self-Editing Search Agent for Efficient RAG)
  • 2026-04-10: LlamaIndex’s LiteParse: Agentic Document Processing and the End of Frameworks Clip title: LiteParse - The Local Document Parser Author / channel: LlamaIndexs LiteParse Agentic Document Processing and the End of)
  • 2026-04-10: Nano Banana 2: JSON Control for Precise AI Image Editing in Gemini Clip title: Nano Banana 2: The JSON Control Hack Author / channel: Nano Banana 2 JSON Control for Precise AI Image Editing in Gemini)
  • 2026-04-10: Structured AI Context: Beyond RAG Limitations with Map-First Architecture Clip title: stop uploading files to AI (use this system instead) Author / channel: Ante AI Portas URL: (Structured AI Context Beyond RAG Limitations with Map-First Architecture)

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