Hallucination-prevention RAG system with verbatim span extraction. Ensures all generated content is grounded in source documents with exact citations.

Reliable research infrastructure for AI agents. Evidence-backed web search with citations, confidence scores, and Clarity anti-hallucination. MCP server, REST API, Python SDK.

re!think it. System prompt teaching LLMs to execute two core tasks: complex answers without hallucinations, and creative ideas without clichés. Written in math-like logic, which LLMs parse better than plain language. Built for mid-to-high complexity tasks, featuring a Bypass branch to execute simple prompts directly without added cognitive overhead

Stop hallucinations by verifying citations.

Deterministic policy language for AI agents. Z3 + TLA+ dual-engine formal verification. Runtime enforcement <1ms.

Comprehensive guide to building production AI agent systems - Scale by Subtraction methodology

AI agent skill that creates formal, verifiable proofs of claims — every fact computed or cited, never asserted

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.

Legality-gated evaluation for LLMs, a structural fix for hallucinations that penalizes confident errors more than abstentions.

Axioma-Omega Protocol v3: Deductive AI reasoning grounded in verified domain truths. Universal adapter for any AI model (Ollama, OpenAI, Gemini, Claude, HuggingFace). Eliminates hallucinations by design.

The missing knowledge layer for AI agents. Curated, agent-readable context for trading, healthcare, legal, and more.

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.

Google ADK + MCP server with security armour: prompt injection defense & hallucination guardrails

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.

Detects hallucination-risk patterns like boolean traps and magic literals to improve AI comprehension

Self-corrective Agentic RAG with LangGraph - eliminates hallucinations through intelligent relevance grading before answering. Features Streamlit UI, MCP server integration & multi-turn memory.

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.