back_office_ops · education · workflow
ChatLTV: a RAG-powered AI faculty co-pilot for Harvard's MBA entrepreneurship course
Professor Bussgang had accumulated a large corpus of course material — cases, teaching notes, books, blog posts, and years of Slack Q&A — but no scalable way to make it interactively accessible to students for case preparation. Faculty also lacked time to provide individualized written feedback on final papers.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · Student submits query in Slack
Students engage with ChatLTV in Slack either privately or publicly.
Tools used
ChatLTVChatGPT v4PineconeLangchainAzure OpenAI ServiceHBS LTV Feedback
Outcome
Over half the class — roughly 170 students — made over 3000 queries during the semester, with nearly 40% giving a quality score of 4 or 5. Faculty gained insight into student understanding before each class via the admin CMS. A custom GPT enabled written feedback on around 125 final papers, something that had not been done before due to time constraints.
Results
Time savedtwo hours
Volumeroughly 170
Running sinceearly September
Grounding & classification
Source type: technical build writeup
37 fields verified against source quotes, 1 dropped as unverifiable.
chatbotcontent generationconversational aiknowledge searchragchat transcriptknowledge basebuilder submittedhuman review describedmetric backednamed customerproduction runtime claimedworkflow describededucationcustomer satisfactionemployee productivitythroughput increasetechnical build writeupback office opsrag answering