The AI Contract Runtime
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Updated
Mar 22, 2026 - Python
The AI Contract Runtime
A muti-agent Dnd DM that directs you to dive into a fantasy world!
Structure-first pipeline that transforms GitHub repositories into question-indexed, graph-grounded semantic assets for code search, Graph-RAG, and LLM-based reasoning.
Open-source harness distillation: frontier teachers improve prompts, tools, validators, skills, and runtime policies around weaker models.
PromptGuard is a pragmatic, opinionated framework for establishing continuous integration for LLM behavior. It operates on a simple, verifiable principle: run the same prompts across multiple model configurations, compare outputs against defined expectations, and flag semantic regressions.
A hands-on technical book and Python lab for building and evaluating agentic systems.
Prototype for execution-plan-driven data standardization, schema profiling, validation, and preview generation.
A conservative, agentic RAG system with evidence-bounded synthesis, citation enforcement, and zero hallucination tolerance.
Solutions to CMU 11-868 Large Language Model Systems assignments — efficient attention/kernels, memory & quantization optimizations, and parallel training/serving — implemented and CPU-verified
Production-grade Retrieval-Augmented Generation (RAG) system for evidence-grounded equity research over SEC filings and live market news.
Research-grade neuro-symbolic RAG framework where retrieval is a policy, not a vector search, built for evaluation, ablation, and reliability analysis.
Reference Python kernel for CIMT: no-meta, observable-only certification machinery for affordance-compiled LLM-integrated systems.
Explicit control and observability over when an LLM should answer, hedge, or refuse — treating generation as a governed system layer, not a side effect of retrieval.
Failure-first analysis of retrieval-augmented and agentic systems, focused on isolating and attributing failures across retrieval, planning, execution, memory, and policy layers.
A unified, production-grade LLM training and alignment framework. Features modular implementations for downstream tasks BERT, DPO, and GRPO to streamline model alignment and optimize memory overhead.
Building reliable LLM knowledge systems with Retrieval-Augmented Generation (RAG), retrieval evaluation, semantic search, and production-oriented AI engineering.
Portfolio-ready AI research monorepo spanning LLM evaluation, agentic AI, reinforcement learning, recommender systems, multimodal AI, vision, NLP, tabular ML, time series, and MLOps labs.
Local-first governed LLM support workflow with signed webhooks, guardrails, human approval, audit trails, cost controls, and deterministic evaluation paths.
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