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

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

The citation verification API.

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

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

Stop AI from hallucinating code. Rules + Hooks + Guards for Claude Code & Codex.

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.

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.

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

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

A Full Stack, RAG application which acts as a workspace for students to store their study material and chat with it.

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

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.

Democratic governance layer for LangGraph multi-agent systems. Adds voting, consensus, adaptive prompting & audit trails to prevent AI hallucinations through collaborative decision-making.