groundedness

Provider-agnostic enterprise RAG and agent evaluation harness for Azure Foundry, vLLM, Ollama, and local demos.

Evaluation patterns, release gates, and anti-hallucination techniques for developer-focused AI workflows.

Merge-blocking evaluation gates for generative AI features: YAML suites run against any HTTP endpoint or Python callable, fail the build on a miss, and emit both a diffable JSON pack and a reviewer document cross-referenced to California's published GenAI risk framework. Aligned to, never approved by.

In-build reference implementation: an offline-first plant-care assistant and public evaluation harness with cited-corpus answers, calibrated abstention, toxicity guardrails, EN/ES parity, photo plant ID, and local reminders.

Single-agent, evidence-grounded claim verification to catch LLM hallucinations — a pluggable fact-gate for agent-arena and any multi-agent system (CrewAI, AutoGen, LangGraph).

Fail-closed evaluation harness for government-facing chat systems: reproducible, provenance-stamped audit verdicts, byte-identical across Python 3.11 to 3.14, with no third-party dependencies. A silent or unreadable target scores zero rather than passing by absence. Two public projects pin it by exact commit.

Bilingual reduced-fare policy assistant for eighteen California transit agencies, grounded in dated citations and shipped with a public 385-case evaluation harness and a committed regression baseline.

A local groundedness judge for RAG: QLoRA-distilled to match a frontier judge 100% at $0/call. Ships only if its own evals beat baseline.

RAG evaluation workbench for retrieval recall, citation coverage, groundedness checks, and failure analysis

An AI-powered RAG groundedness / hallucination checker - segments an LLM answer into atomic claims and flags those NOT supported by the retrieved context, via an LLM judge. Provider-agnostic, BYO-key, offline --dry-run. Python CLI.

Your RAG, put to proof — self-correcting answers and CI eval gates that block the merge when hallucination rate crosses your threshold

RAG that grades every answer: groundedness, helpfulness, accuracy + citations. FastAPI + live browser demo

Scores whether an AI agent's answer is actually supported by the tool results it had access to. An LLM-as-judge harness for catching ungrounded or fabricated claims in agent outputs.

TypeScript eval harness for measuring whether Grok answers stay grounded in source evidence

Deterministic citation and claim-support checks for RAG evaluation datasets.

Detect & score LLM hallucinations by groundedness — labeled data, precision/recall/F1, runs offline with no API key. Pluggable LLM-judge backends.

What the four-box RAG diagram leaves out hybrid retrieval, chunking, security trimming, and groundedness evaluation, as runnable Azure AI Search samples.

Groundedness & hallucination checker for LLM/RAG answers: decomposes an answer into atomic claims, verifies each against its sources (LLM judge or offline NLI), and highlights what isn't supported.

Bilingual (Arabic/English) medical RAG system with a runtime groundedness gate that blocks unsupported answers before they reach the user.