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.

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

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

Zero-hallucination reference verification for Claude Code — live CrossRef/S2/PubMed verification, verbatim abstract traceability, retraction check

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

Stop hallucinations by verifying citations.

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

W.A.Y? (Who Are You? : I'm not a developer) — a personal AI harness that learns you, not a framework you learn

TrustScoreEval: Trust Scores for AI/LLM Responses — Detect hallucinations, flags misinformation & Validate outputs. Build trustworthy AI.

Deterministic factual substrate for multi-agent AI. Shared evidence-backed facts and reproducible grounding through dataset_hash.

Framework structures causes for AI hallucinations and provides countermeasures

Verification skills for academic research — grounds every claim in fetched evidence, not memory. Citation/figure/contradiction checking, arXiv monitoring, NRF grant writing.

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.

span is a CLI tool for AI agents that moves blocks text addressed by reference to save context and ensure faithful byte-level edits within a file or between multiple files.

Transform probabilistic LLMs into deterministic state machines to improve logic, prevent context loss, and generate unique, reliable responses.

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

A Prompt Engineering project demonstrating Context-Anchored Answering (RAG Basics) using context injection, negative constraints, and citation-based responses to reduce AI hallucinations.

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

Give Claude Code / Codex / any MCP agent real eyes and ears — closed-loop visual/audio/video verification with mandatory enforcement hooks, so agents stop reporting 'done' when it isn't.