RAG: AI on Your Own Data training course

RAG versus fine-tuning:

cost, latency, accuracy, and maintenance tradeoffs shown with real benchmark numbers

Document preparation lab:

splitting strategies, cleaning noise, and handling PDFs, Word docs, and HTML in one pipeline

Embedding models compared:

OpenAI ada, open-source alternatives, and a decision framework for choosing based on your data

Vector database setup:

schema design, indexing choices, and running your first similarity search query against real documents

Evaluation lab:

building a test set and scoring retrieval precision, recall, and final answer quality with a repeatable method

Retrieval pipeline build:

query embedding, nearest-neighbour search, and result ranking working together end to end

Prompt engineering for RAG:

context injection patterns that demonstrably reduce hallucination in tested examples

Conflict resolution:

practical strategies for versioned documents, contradictory sources, and stale content in the knowledge base

Packaging and deployment:

wrapping the completed pipeline as a callable API and running it in a cloud environment

 

Enterprise connector lab:

pulling live documents from SharePoint, Confluence, or an internal file store into the pipeline