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C2 / Advanced

Knowledge-Base Engineering & Retrieval Systems

Explore information retrieval theory, text processing & chunking, embeddings & vector search. Work through applied exercises and develop a project you can explain, test, and improve.

A certificate issued by this academy is a certificate of completion only. It is not accredited. It is not approved by the Ohio State Board of Career Colleges and Schools (SBCCS) or any other state agency. No income or employment outcome is promised.

12 modules≈135 learner hoursSelf-paced draft
YOUR TAKEAWAYS

What you will practice

  • Explain the core theory of information retrieval theory.
  • Apply methods from embeddings & vector search.
  • Analyze trade-offs in knowledge graphs & ontologies.
  • Evaluate solutions involving rag evaluation (precision/recall, faithfulness).
  • Design and build packaging kbs as products.
  • Produce the capstone: production vertical KB + eval report, defended to a human reviewer.
THE LEARNING PLAN

Inside the course

  1. 01Information retrieval theory
  2. 02Text processing & chunking
  3. 03Embeddings & vector search
  4. 04Hybrid search & reranking
  5. 05Query understanding & RAG architectures
  6. 06Knowledge graphs, ontologies & structured retrieval
  7. 07Data quality, provenance & versioning
  8. 08RAG evaluation — precision, recall, faithfulness
  9. 09Access control, privacy & security
  10. 10Production operations — pipelines, latency, cost & observability
  11. 11Domain knowledge bases — insurance, real estate, tax
  12. 12Packaging KBs as products & capstone integration
TRY THE MATERIAL

A little of what is inside.

EXCERPT FROM THE STUDENT MATERIAL
A knowledge base (KB) is only as useful as the answers someone can get out of it. A company can own ten thousand well-written pages about septic permits, insurance riders, and service procedures, and still fail its customers if the right page does not surface when the question is asked. This course treats knowledge-base engineering as an applied discipline built on information retrieval (IR): the study of finding material, usually documents, of an unstructured nature that satisfies an information need from within large collections. Every term in that definition matters for what follows.

Excerpt from C2. The full curriculum is not publicly downloadable in this preview.