Knowledge Base and RAG Workshop

A $1,200 workshop for building source-grounded knowledge workflows. Covers document readiness, chunking strategy, retrieval quality, citation expectations, answer evaluation, permissions, and maintenance cadence. Designed for internal knowledge bases, support desks, policy libraries, and research workflows. Core sources: - https://arxiv.org/abs/2005.11401 - https://platform.openai.com/docs/guides/retrieval - https://platform.openai.com/docs/guides/evals

Curriculum

1.
RAG problem framing
When retrieval helps, when it does not, and how to define answer quality before building indexes.
2.
Document readiness audit
Owner, freshness, structure, duplicates, contradictions, access level, and source-of-truth rules.
3.
Chunking and metadata
How segmentation, titles, dates, owners, tags, and document type affect retrieval quality.
4.
Citations and answer contracts
Designing outputs that show sources, uncertainty, follow-up needs, and unsupported claims.
5.
Retrieval eval sets
Build test questions, expected sources, negative cases, ambiguous cases, and regression checks.
6.
Permissions and privacy
Preventing cross-team leakage, overbroad indexes, stale exports, and sensitive source exposure.
7.
Maintenance cadence
Refresh schedules, source deprecation, quality review, and feedback loops from real usage.