Build RAG systems, design knowledge architectures, and implement AI backend logic step by step

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• 1

  Build AI Knowledge Systems with RAG: From Ingest to Backend

  • PROMO COURSE Free Preview
• 2

  IMPORTANT | Start Here: Introduction and How to Use This Course

  • Introduction Free Preview
  • IMPORTANT | How to Use This Course Free Preview
• 3

  Product Knowledge System

  • 1.0 How Real AI Systems Are Built
  • 1.1 What Is a Product Knowledge AI System
  • 1.2 Configuring an AI Ingestion Pipeline
  • 1.3 Connecting to the Vector Database and Embedding Service
  • 1.4 Turning Product Knowledge into Embeddings
  • 1.5 Designing Stable Chunk Identifiers
  • 1.6 Designing Metadata for Retrieval
  • 1.7 Writing Knowledge to the Vector Database
  • 1.8 Structuring Product Knowledge for AI
  • 1.9 Detecting Content Changes with Hashing
  • 1.10 Building the AI Backend with FastAPI
  • 1.11 Defining the Query Request Model
  • 1.12 Normalizing User Queries
  • 1.13 Performing Semantic Search
  • 1.14 Grouping Retrieved Knowledge by Product
  • 1.15 Building the Context for AI
  • 1.16 Returning the Final API Response
  • 1.17 Testing the Backend with Swagger
  • 1.18 Testing the AI Assistant in Voiceflow
• 4

  Procedural Knowledge System

  • 2.1 — Why Operational AI Systems Need a Different Architecture
  • 2.2 — Architecture of the Operational Knowledge System
  • 2.3 — Designing the Knowledge Model
  • 2.4 — Ingesting Apartment Entities
  • 2.5 — Ingesting Resident Entities
  • 2.6 — Modeling Infrastructure Entities
  • 2.7 Ingesting Operational Procedures
  • 2.8 — Understanding the Query Processing Pipeline
  • 2.9 — Query Normalization
  • 2.10 — Query Rewriting
  • 2.11 — Query Expansion with Synonyms
  • 2.12 — Detecting Apartments and Buildings in Queries
  • 2.13 — Detecting Operational Issues
  • 2.14 — Detecting Query Intent
  • 2.15 — Routing Queries by Operational Category
  • 2.16 — The Search Pipeline
  • 2.17 — Reranking Search Results
  • 2.18 — Fallback Strategies
  • 2.19 — Testing the Backend with Swagger
  • 2.20 — Testing the Operational Assistant in a Chat Interface
• 5

  Research Project (Experimental Lab)

  • 3.1 — Designing Knowledge Structure Before Building the System
  • 3.2 — Anatomy of the Behavioural Analysis Form
  • 3.3 — From Analysis Form to Machine Readable Data
  • 3.4 — Why Behavioural Profiles Must Be Structured
  • 3.5 — Flattening Behavioural Profiles for Reliable Queries
  • 3.6 — Converting Case Sections into Vectorized Chunks
  • 3.7 — Why the System Uses Separate Namespaces
  • 3.8 — Why the Backend Uses Three Query Modes
  • 3.9 — The Fact Retrieval Pipeline
  • 3.10 — The Analytic Query Pipeline
  • 3.11 — The Interpretive Query Pipeline
  • 3.12 — Ranking and Selecting Similar Cases
  • 3.13 — Why Interpretive Results Must Include Explicit Uncertainty
  • 3.14 — How the Entire System Works Together
  • 3.15 — Testing the Research System
• 6

  Building the Ingest Pipeline and Backend

  • 4.0 — Building the Minimal AI Knowledge Backend
  • 4.1 — Why We Are Building a Reusable RAG Template
  • 4.2 — Designing the Knowledge Structure Free Preview
  • 4.3 — Understanding the Ingest Pipeline
  • 4.4 — Environment Configuration
  • 4.5 — Helper Functions in the Ingest Script
  • 4.6 — Creating an Animal Record
  • 4.7 — Upsert Operation
  • 4.8 — Introduction to the Backend API
  • 4.9 — Query Normalization
  • 4.10 — Query Expansion
  • 4.11 — Detecting Metadata Filters
  • 4.12 — Vector Search
  • 4.13 — Ranking Results
  • 4.14 — API Response
  • 4.15 — Testing with Swagger
  • 4.16 — Chat Interface Integration
  • 4.17 — Final Architecture
  • 4.18 — How the Entire AI Knowledge Pipeline Works
• 7

  Testing AI Knowledge Systems

  • Testing AI Knowledge Systems in Practice
• 8

  Foundations of Retrieval-Augmented Generation

  • 7.1 Why Knowledge Systems Need RAG
  • 7.2 What Retrieval Augmented Generation Is
  • 7.3 Embeddings and Semantic Meaning
  • 7.4 Vector Search and Similarity
  • 7.5 Chunking and Knowledge Representation
  • 7.6 The Complete RAG Pipeline
  • 7.7 Common Failure Modes in RAG Systems
• 9

  AI Governance and Responsible AI Systems

  • 6.1 AI Systems as Decision Support Infrastructure Free Preview
  • 6.2 Decision Support vs Decision Automation
  • 6.3 What AI Governance Actually Means
  • 6.4 Risk Based Design of AI Systems
  • 6.5 Governance by Design
  • 6.6 Narrow Scope AI Systems
  • 6.7 Data and Knowledge Governance in AI Systems
  • 6.8 Human Oversight and Accountability in AI Systems
  • 6.9 Technical Enforcement of AI Governance
  • 6.10 Designing an AI Governance Framework
  • 6.11 Procedural AI Systems in Practice
  • 6.12 From RAG Systems to Governed AI Systems Free Preview
• 10

  Human–AI Decision Laboratory

  • 8.1 Pattern Recognition, Human Cognition, and AI Knowledge Systems Free Preview
  • 8.2 Why Naive RAG Fails in Complex Analytical Domains
  • 8.3 Structured Knowledge Representation From Narrative to Analytical Structure
  • 8.4 Hybrid Retrieval Architecture Beyond Standard RAG
  • 8.5 Query Routing and AI Decision Logic
  • 8.6 Human–AI Analytical Collaboration
  • 8.7 Interpreting AI Outputs Uncertainty, Evidence, and Analytical Discipline
• 11

  Conclusion

  • System Scope and Limitations
  • Conclusion