12 results for “topic:hallucination-prevention”
Hallucination-prevention RAG system with verbatim span extraction. Ensures all generated content is grounded in source documents with exact citations.
Comprehensive guide to building production AI agent systems - Scale by Subtraction methodology
TrustScoreEval: Trust Scores for AI/LLM Responses — Detect hallucinations, flags misinformation & Validate outputs. Build trustworthy AI.
Framework structures causes for AI hallucinations and provides countermeasures
A robust RAG backend featuring semantic chunking, embedding caching, and a similarity-gated retrieval pipeline. Uses GPT-4 and FAISS to provide verifiable, source-backed answers from PDFs, DOCX, and Markdown.
Theorem of the Unnameable [⧉/⧉ₛ] — Epistemological framework for binary information classification (Fixed Point/Fluctuating Point). Application to LLMs via 3-6-9 anti-loop matrix. Empirical validation: 5 models, 73% savings, zero hallucination on marked zones.
A Full Stack, RAG application which acts as a workspace for students to store their study material and chat with it.
An epistemic firewall for intelligence analysis. Implements "Loop 1.5" of the Sledgehammer Protocol to mathematically weigh evidence tiers (T1 Peer Review vs. T4 Opinion) and annihilate weak claims via time-decay algorithms.
Self-corrective Agentic RAG with LangGraph - eliminates hallucinations through intelligent relevance grading before answering. Features Streamlit UI, MCP server integration & multi-turn memory.
Legality-gated evaluation for LLMs, a structural fix for hallucinations that penalizes confident errors more than abstentions.
Semantic Processing Unit (SPU): A neurosymbolic AI architecture replacing token prediction with differentiable matrix operators. It guarantees 100% logical accuracy, structural safety, and zero-error invariants on OOD data by decoupling semantic parsing from hardware-accelerated matrix algebra.
Democratic governance layer for LangGraph multi-agent systems. Adds voting, consensus, adaptive prompting & audit trails to prevent AI hallucinations through collaborative decision-making.