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Building Retrieval-Augmented Generation Systems for Professionals

7 chapters · 33 lessons

1. 1. RAG Architecture Fundamentals

   4 lessons

   Understand how retrieval-augmented generation works and when to use it over fine-tuning or raw prompting.

2. 2. Document Processing and Embedding Strategy

   5 lessons

   Build pipelines that transform domain documents into searchable, semantically meaningful representations.

3. 3. Vector Search and Retrieval Implementation

   5 lessons

   Implement efficient vector databases and retrieval algorithms that surface the right context at query time.

4. 4. Access Control and Security Layers

   4 lessons

   Design permission systems that respect organizational boundaries and regulatory requirements in AI outputs.

5. 5. Prompt Engineering for Grounded Generation

   5 lessons

   Craft prompts and system instructions that use retrieved context effectively and signal uncertainty appropriately.

6. 6. Evaluation and Quality Assurance

   5 lessons

   Measure RAG system performance using domain-appropriate metrics and build human review into the workflow.

7. 7. Workflow Integration and Deployment

   5 lessons

   Connect RAG systems to existing professional tools and build interfaces that fit how domain experts work.