hallucination-reduction

Reduce hallucinations through first-principles reasoning, verification, self-critique, and explicit uncertainty for AI agents.

Anti-hallucination research skill for Claude Code — admits uncertainty, extracts direct quotes before analysis, cites every claim, retracts unverifiable statements. Based on Anthropic's official guardrail techniques. By TheGEOLab.net

About Essays, and Poc on using runtime evidence to build cleaner code context for AI to reduce hallucinations.

Genesis Governance OS The Operating System for Multi-Agent AI Inspired by Political Science, built to coordinate intelligent agents at scale.

Why Pure Vector Search is a "False Proposition" for RAG?

Self-healing RAG system that retrieves, verifies, and grades its own answers. Automatically rewrites queries and retries when outputs are weak, ensuring accurate, hallucination-free responses.

Hallucination-prune multiagent RAG for pharmaceutical knowledge bases

Dependency-free evidence core for AI agents: observation envelopes, provenance, memory continuity and claim gates to reduce hallucination drift.

Developer-first prompt engineering patterns for grounded, testable, and reliable AI outputs.

Autonomous AI research agent using LangGraph to eliminate LLM hallucinations via a Generate-Critique-Refine self-reflection loop.

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.

System prompt that enforces strict compliance, self-auditing, and hallucination reduction in any LLM. Time-anchored, evidence-declared, confidence-scored, release-gated.

An agentic, self-correcting RAG pipeline that extracts claims from generated answers, verifies them using Qdrant and NLI, and automatically repairs hallucinated facts.

Prompt engineering framework + evaluation harness for LLM workflows (classification, summarization, extraction).

A hallucination-resistant Retrieval-Augmented Generation (RAG) system.

An RLHF-inspired DPO framework that explicitly teaches LLMs when to refuse, significantly reducing hallucinations.

Professional cross-agent answer quality gate for improving AI responses: intent match, evidence, assumptions, verification, brevity, and usefulness.

Fine-tuning Llama 3.1 8B for a feedback-to-engineering-insights pipeline — LoRA fine-tuning, local inference via FastAPI + ngrok, n8n orchestration, and DeepEval evaluation showing reduced hallucination on vague feedback.

Runtime patch that kills LLM loops, drift & hallucinations in real-time – works with any model (GPT, Grok, Claude, Llama, Mistral…)

Policy-constrained LoRA fine-tuning to reduce hallucinations in a billing-focused LLM, using a PayFlow (fictional SaaS) use case with before–after evaluation.