RAG AI Tutor & LLM Evaluation Framework

Case Study Summary

Organization: Emeritus
Industry: EdTech / Higher Education
Role: Senior Data Scientist

Impact Metrics:

Evaluation framework that secured enterprise AI contracts with Stanford, MIT and Berkeley
AI Tutor in production, answering questions from learners on live university courses
Automated validation gates and hallucination-risk controls aligned to academic-integrity requirements

Universities hold their programs to a high standard of academic integrity. Before they would put an AI assistant in front of their learners, they needed evidence that it would answer accurately, stay grounded in course material and not make things up.

Challenge

There were two problems to solve together:

Build a tutor learners can trust. Answer questions about unstructured course content quickly, accurately and at a sustainable inference cost.
Prove it. Give university partners a rigorous, repeatable way to measure answer quality and hallucination risk, tied to their own objectives.

Approach

LLM/RAG evaluation framework. I engineered a Python evaluation framework with:

Retrieval metrics (is the right course content being found?) and generation metrics (is the answer correct and grounded?)
Automated validation gates that block changes that reduce quality
Hallucination-risk controls aligned to client objectives around academic integrity

This framework became the assurance layer that secured enterprise AI contracts with Stanford, MIT and Berkeley.

AI Tutor. I built and shipped a LangChain-orchestrated RAG service:

Embeddings-based vector retrieval over unstructured course content
Iterative prompt engineering, measured against the evaluation framework
Served into the live learning platform via API, balancing answer quality against latency and inference cost

Solution Overview

flowchart LR
    subgraph ingest [Ingestion]
        direction TB
        A[Course content] --> B[Parsing & chunking] --> C[Embeddings] --> D[(Vector index)]
    end
    subgraph serve [Serving]
        direction TB
        E[Learner question] --> F[LangChain<br/>orchestration] --> G[LLM answer] --> H[Learning platform API]
    end
    subgraph evaluate [Evaluation]
        direction TB
        I[Retrieval &<br/>generation metrics] --> J([Validation gates &<br/>hallucination checks])
    end
    ingest -- retrieval --> serve
    serve -. scored by .-> evaluate

Results & Impact

Enterprise AI contracts secured with Stanford, MIT and Berkeley, backed by measurable quality evidence.
AI Tutor in production, serving learners enrolled in university courses such as MIT and Stanford programs.
A repeatable evaluation process, so every prompt or retrieval change is validated before it ships.

Tech Stack

Python
LangChain
Sentence embeddings and vector search
LLM evaluation (retrieval and generation metrics)
API-based serving into the learning platform
Google Cloud Platform