hallucination-prevention

Agentic RAG for local and self-hosted document search: hybrid retrieval, reranking and multimodal RAG on embedded LanceDB, with Docling parsing and an MCP server

Provenance-first extractive RAG: return verbatim source spans with citations using local ModernBERT or optional LLM-assisted extraction.

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

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

Four patterns for agent systems you can constrain, observe, and verify. Each states an invariant that can fail, and ships the test that fails with it.

Stop hallucinations by verifying citations.

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

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

AI advisory board as a Claude Code skill + MCP server: verifies every quote word-for-word against public-domain sources, or abstains. Fail-closed fidelity. | Совет советников как скилл для Claude Code и MCP-сервер: каждая цитата сверяется дословно с источником в общественном достоянии, иначе отказ.

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

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

Catch AI code hallucinations—fabricated packages, invented APIs, and contradicted behavior—deterministically, without trusting another model.

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

Build a Production RAG System with Python, LangChain, and ChromaDB Build ShopBot from scratch — a production RAG assistant that answers from retrieved evidence, not training memory. Ten chapters, one complete system.

Event-driven, enterprise-ready RAG engine built on Django. A multi-layer "Agentic Sieve" (keywords → predictive queries → chunks) with a Critic validation step cuts retrieval latency and hallucinations.

Developer toolkit for EthersFlow — a multi-model trust layer that verifies AI outputs through adversarial consensus. MCP server, SDKs, and API docs.

Framework structures causes for AI hallucinations and provides countermeasures

10ms local neurosymbolic anti-hallucination & truth-enforcement engine for AI coding agents

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.

A simple prompt to instruct AI models to state their limitations when they can't fulfill a request