What is this course about?
A campus is a genuinely hard retrieval target: rules are effective-dated, documents contradict each other, and the authoritative answer depends on when the student enrolled. This course builds RAG that handles all three, with citation and provenance so a student can verify the answer and a registrar can defend it.
Who is this course for?
- Academic technology engineers and developers
- Data and integration architects
- Registrars partnering on catalog structure
- IT leaders evaluating build versus buy
What do I need before starting?
- Comfort with APIs and structured data
- Basic familiarity with embeddings and vector search
What will I be able to do afterwards?
- Chunk structured institutional documents without destroying their meaning
- Implement effective-dated retrieval that answers by the student's catalog year
- Detect and surface contradiction between authoritative documents
- Attach citation and provenance so any answer can be verified
- Build a registrar-authored gold set and evaluate retrieval against it
What does each module cover?
Why is a campus a hard retrieval target?
45 minThe three properties — effective dating, exceptions, contradiction — that break naive RAG on institutional content.
Objectives
- Characterize the properties that distinguish campus content
- Predict where a naive implementation will fail
- Set evaluation criteria before building
Topics
Activity. Take ten real policy questions and predict which a naive RAG system would get wrong.
How do you chunk a course catalog?
60 minChunking structured documents — requirement trees, tables, and cross-references — without severing meaning.
Objectives
- Choose chunking strategies by document structure
- Preserve table and requirement-tree integrity
- Handle cross-references between chunks
Topics
Activity. Chunk a real catalog section three ways and compare retrieval quality on the same questions.
How do you answer by catalog year?
60 minEffective-dated retrieval — the campus-specific capability no general RAG tutorial covers.
Objectives
- Model effective dates as retrievable metadata
- Filter retrieval by the student's applicable edition
- Handle questions that span multiple editions
Topics
Activity. Implement edition filtering and verify against a question set with known per-year answers.
What happens when the catalog and the handbook disagree?
55 minContradiction detection and authority ranking, rather than silently blending conflicting sources.
Objectives
- Establish a document authority hierarchy
- Detect contradiction between retrieved chunks
- Surface the conflict rather than resolving it silently
Topics
Activity. Seed a deliberate contradiction and verify the system surfaces rather than averages it.
How do you attach citation and provenance?
50 minMaking every answer verifiable — the property that determines whether staff will trust the system.
Objectives
- Attach source document, section, and effective date to every answer
- Render citations a student can act on
- Detect when an answer is not adequately grounded
Topics
Activity. Add provenance to your pipeline and test abstention on questions with no grounded answer.
How do you build a gold set registrars trust?
55 minEvaluation data authored by the people who know the correct answers.
Objectives
- Recruit domain authors and structure their contribution
- Write gold questions with defensible correct answers
- Version the gold set as policy changes
Topics
Activity. Run a gold-set authoring session with a registrar and produce 30 verified items.
What breaks when the catalog is republished?
50 minRefresh pipelines and the regression testing that catches silent degradation.
Objectives
- Design an ingestion pipeline for document republication
- Detect regression after a content refresh
- Handle partial and rolling updates
Topics
Activity. Republish a modified catalog and run the regression suite to catch what degraded.
Building a policy agent that cites its source
75 minThe hands-on module: a complete policy agent with effective dating, contradiction surfacing, and provenance.
Objectives
- Assemble the full pipeline end to end
- Pass the gold set at an agreed threshold
- Document the architecture for handover
Topics
Activity. Deploy the complete agent and pass the registrar-authored gold set.
What is the capstone project?
Institutional policy agent with a registrar-verified gold set
Build a complete retrieval system over your institution's catalog and handbook, with effective-dated retrieval, contradiction surfacing, provenance, and a registrar-authored evaluation set it must pass.
Deliverable: A deployed agent, its architecture documentation, and a versioned gold set with passing scores.
How are learners assessed?
- Gold set performance against a pre-agreed threshold
- Contradiction handling verified with seeded conflicts
- Architecture documentation reviewed for handover sufficiency
What ships with the course?
Facilitator guide
Session-by-session running order, discussion prompts, and the questions that reliably derail a room.
Learner workbook
Exercises, checklists, and the templates each module's activity produces.
Hands-on lab environment
A sandboxed ibl.ai deployment so exercises run against real agents, not screenshots.
Assessment bank
Scenario questions and rubric criteria mapped to each stated learning outcome.
Source bibliography
Every primary regulation and standard cited on this page, linked and dated.
Which AI agents does this course use?
The hands-on modules run against agents already deployable on the ibl.ai platform for higher education.
Where does the course material come from?
Every module is grounded in primary sources — the regulation, standard, or research itself, not a summary of it. Each was resolved at authoring time.
- Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
Lewis et al., arXiv
The foundational RAG paper, used for the architectural grounding in Module 1.
- Model Context Protocol
Anthropic
Integration layer for connecting the agent to SIS and catalog systems.
- LlamaIndex
LlamaIndex
Reference implementation patterns for structure-aware chunking.
- AI Risk Management Framework
NIST
Frames the groundedness and abstention requirements in Module 5.
Delivery notes
Binding guidance for anyone preparing and delivering this course.
- This is the most technical course in the higher-ed catalog and should be scoped as such. Do not dilute it for a mixed audience — run HE-1 or HE-2 for non-technical stakeholders instead.
- Module 3's effective-dated retrieval is the genuinely novel content. Every general RAG course covers chunking; almost none covers temporal authority. Give it the most build time and the best examples.
- The gold set session in Module 6 needs a real registrar in the room. Engineers consistently write gold questions that are too easy and miss the exceptions that matter.
- Use a real catalog with at least three years of drift. A synthetic one will not contain the messy substitutions and grandfathered rules that make this hard.
- Ship the regression suite from Module 7 as a reusable artifact — it is the piece institutions are least likely to build for themselves.
Why run AI training on a platform you own?
You own the course, not a licence to it
Course content, learner data, and the platform run inside your perimeter — you own all the code and the data.
Model-agnostic delivery
Run the course's AI components on any LLM — Claude, GPT, Llama, Gemini, Command — and switch anytime.
No per-seat training licences
Usage-based or self-hosted, so cost tracks actual use rather than headcount.
Deploy anywhere
Cloud, private VPC, on-premise, or fully air-gapped — including for cohorts that cannot use public AI tools.
Frequently asked questions
What does the RAG on Institutional Knowledge: Catalogs, Policies, Handbooks course cover?
A campus is a genuinely hard retrieval target: rules are effective-dated, documents contradict each other, and the authoritative answer depends on when the student enrolled. This course builds RAG that handles all three, with citation and provenance so a student can verify the answer and a registrar can defend it. It runs 7 hours across 8 modules across 8 modules, at advanced level, and closes with a capstone: Institutional policy agent with a registrar-verified gold set.
Who should take RAG on Institutional Knowledge: Catalogs, Policies, Handbooks?
It is written for Academic technology engineers and developers, Data and integration architects, Registrars partnering on catalog structure, IT leaders evaluating build versus buy. Prerequisites: Comfort with APIs and structured data; Basic familiarity with embeddings and vector search.
Can we run this course on our own infrastructure?
Yes. ibl.ai is model-agnostic and deploy-anywhere — cloud, private VPC, on-premise, or fully air-gapped — and you own all the code and the data. Cohort data, submissions, and any material learners upload stay inside your perimeter, which matters for higher education teams that cannot send work to a public AI tool.
How do we get access to RAG on Institutional Knowledge: Catalogs, Policies, Handbooks?
Request access and we will set it up for your cohort — hosted by ibl.ai, or running against your own deployment. Tell us the group size and timing you need, and whether it should run inside your own perimeter.
How much does AI training for higher education cost on ibl.ai?
There is no per-seat pricing — you pay for usage or self-host and pay only for the infrastructure, so a 5,000-person rollout does not cost 5,000 licences. 1.6M+ users across 400+ organizations run the platform this way, including NVIDIA, MIT, and Syracuse University.