Enterprise Retrieval-Augmented Generation with C#

Building Production-Grade AI Applications in the .NET Ecosystem

Introduction: The Enterprise RAG Challenge

Chapter 1: Foundations of RAG and the .NET AI Ecosystem

1. What RAG Actually Solves (and What It Doesn’t)
2. The Evolution from Prompt Engineering to Retrieval-Augmented Systems
3. The Modern .NET AI Stack: Microsoft.Extensions.AI, Semantic Kernel, and Agent Framework
4. Choosing Your LLM Provider: Azure OpenAI, OpenAI API, and Local Models
5. A Quick-Start RAG App in .NET
6. From Prototype to Production: Refactoring into a Layered Architecture
7. Key Takeaways

Chapter 2: Embeddings and Vector Representations

1. How Embeddings Encode Meaning
2. Choosing an Embedding Model: Accuracy vs. Speed vs. Cost
3. Domain-Specific Embeddings and Fine-Tuning
4. Dimensionality, Quantization, and Storage Efficiency
5. Evaluating Embedding Quality
6. Key Takeaways

Chapter 3: Vector Databases for .NET Developers

1. The Landscape of Vector Stores in 2026
2. Qdrant: Performance and Operations
3. PostgreSQL + pgvector: Consolidation and Simplicity
4. Azure AI Search: Managed Hybrid Search
5. Microsoft.Extensions.VectorData Abstraction Layer
6. Making the Choice: A Decision Framework
7. Key Takeaways

Chapter 4: Building Robust Ingestion Pipelines

1. The Ingestion Pipeline Architecture
2. Document Parsing and Layout-Aware Extraction
3. Chunking Strategies: Fixed, Recursive, Semantic, and Structural
4. Metadata Enrichment and Classification Tags
5. Incremental Ingestion and Change Detection
6. Key Takeaways

Chapter 5: Hybrid Search and Retrieval Quality

1. Why Dense Vector Search Is Not Enough
2. Hybrid Search with RRF in Azure AI Search
3. Cross-Encoder Reranking
4. Query Transformation Techniques
5. Designing a Production Retriever
6. Key Takeaways

Chapter 6: Prompt Engineering for Grounded Generation

1. The Anatomy of a RAG Prompt
2. Context Assembly Strategies: Write, Select, Compress, Isolate
3. Citation and Grounding Techniques
4. Anti-Hallucination Guardrails in Prompts
5. Prompt Versioning and Experimentation
6. Key Takeaways

Chapter 7: Agentic RAG with the Microsoft Agent Framework

1. From RAG to Agentic RAG
2. Microsoft Agent Framework 1.0: Architecture and Primitives
3. Sequential and Concurrent Workflows
4. Agentic Retrieval Patterns
5. Human-in-the-Loop and Approval Gates
6. Key Takeaways

Chapter 8: Security, Privacy, and Governance

1. The RAG Security Threat Model
2. Document-Level Access Control at Retrieval Time
3. PII and PHI Redaction Pipelines
4. Prompt Injection and Content Guardrails
5. Audit Trails and Compliance
6. Key Takeaways

Chapter 9: Evaluation Frameworks for RAG Systems

1. What to Evaluate in a RAG System
2. Retrieval Metrics: Precision, Recall, MRR, NDCG
3. Generation Metrics: Faithfulness, Groundedness, Answer Relevance
4. Microsoft.Extensions.AI.Evaluation in Practice
5. Building Custom Evaluators and CI/CD Gates
6. Key Takeaways

Chapter 10: Observability and Debugging RAG Systems

1. The Observability Challenge in RAG
2. End-to-End Tracing with OpenTelemetry
3. Metrics and Dashboards
4. Root-Cause Analysis Playbook
5. Debugging Tools and Techniques
6. Key Takeaways

Chapter 11: Scalability and Performance Optimization

1. The Latency Budget Problem
2. Semantic Caching Strategies
3. Async Pipelines and Concurrent Retrieval
4. Model Routing and Tiered Inference
5. Infrastructure Scaling Patterns
6. Key Takeaways

Chapter 12: Testing and Quality Assurance

1. What to Test in a RAG System
2. Unit Testing Retrieval and Generation
3. Integration Testing with Testcontainers
4. Regression Evaluation and Golden Datasets
5. A/B Testing and Prompt Experiments
6. Key Takeaways

Chapter 13: Deployment, CI/CD, and Cost Management

1. Containerizing a RAG Application
2. CI/CD Pipelines for AI Applications
3. Kubernetes Deployment Patterns
4. Production Runbooks and Incident Response
5. Cost Management and Token Budgeting
6. The Complete Architecture: Putting It All Together
7. Key Takeaways

Conclusion: Building the Next Generation of Enterprise Knowledge Systems

References