Design enterprise-ready GenAI architectures with LLMs, prompt engineering, RAG, fine-tuning, Vector DBs, semantic search, .NET, Ollama, OpenAI, Azure OpenAI, and Azure AI Search for production apps and modern support workflows. Key Features
Design enterprise GenAI apps using LLMs, prompt engineering, RAG, fine-tuning, and Vector DBs
Build .NET workflows with OpenAI, Ollama, Qdrant, Azure OpenAI, and Azure AI Search services
Choose prompts, RAG, or fine-tuning by quality, latency, cost, context, and deployment fit for apps
Book Description Enterprise teams are moving beyond chatbot integrations toward AI features that reason over business knowledge, automate support workflows, and produce grounded responses. To deliver this, you need to connect LLMs, prompts, retrieval, embeddings, Vector DBs, and cloud AI services. This book guides you through generative AI architecture for an eShop support application. You will begin with GenAI foundations, transformers, tokens, LLMs and SLMs, inference settings, function calling, and structured outputs. You will then compare OpenAI, Llama, Claude, Gemini, Mistral, Gemma, Phi, Hugging Face, and Ollama, choosing models by quality, speed, price, latency, context, and deployment needs. Next, you will design prompts for classification, summarization, sentiment analysis, Q&A, and ticket handling before building RAG pipelines with ingestion, embeddings, vector search, reranking, and generation. You will explore Qdrant, Pinecone, Chroma, Weaviate, PgVector, and Redis, then connect these patterns to .NET, OpenAI, Azure OpenAI, and Azure AI Search. You will learn when fine-tuning, PEFT, LoRA, or transfer learning is better than prompts or RAG. By the end of the book, you'll be able to design LLM-powered architectures for enterprise support, semantic search, summarization, classification, and knowledge retrieval. What you will learn
Understand GenAI, transformers, tokens, LLMs, SLMs, function calling, and structured outputs
Compare OpenAI, Llama, Claude, Gemini, Mistral, Gemma, Phi, Hugging Face, and Ollama
Select models by quality, speed, price, latency, context window, and deployment needs
Design prompts for chat, Q&A, classification, summarization, sentiment analysis, and tickets
Build RAG pipelines with ingestion, embeddings, vector search, reranking, and generation
Use Qdrant, Pinecone, Chroma, Weaviate, Milvus, PgVector, and Redis for semantic search
Who this book is for This book is for data engineers, AI engineers, and technical managers evaluating RAG, Vector DBs, semantic search, and fine-tuning for product or platform use cases. It is also for software developers, .NET engineers, solution architects, cloud-native developers, AI application engineers, and technical leads who want to integrate AI-powered LLMs into enterprise apps. To get the most from this book, readers should have basic knowledge of data workflows, AI concepts, app development, APIs, and architecture; prior .NET, microservices, or cloud experience will help but is not required.
