About the Book
Build reliable, production-ready retrieval-augmented generation with the Hugging Face stack, from embeddings and multimodal search to enterprise-scale serving.Many teams ship prototypes that fail under real data, compliance needs, and traffic. This book closes that gap by showing how to turn retrieval-augmented generation into a dependable system that scales. You will learn how to choose and evaluate embeddings, design hybrid search, integrate rerankers, and serve models with low latency, all while meeting security and governance requirements. The content maps tightly to practical workflows that developers and architects can adopt without guesswork.
Whether you support clinicians with grounded answers, help analysts navigate regulations, or power customer search across large catalogs, you need pipelines that are accurate, observable, and cost-aware. This guide offers clear patterns, real industry scenarios, and tested components that slot into modern stacks. The result is faster time to production with fewer surprises when you scale.
What you will learn
Design end-to-end RAG pipelines that combine chunking, retrieval, reranking, and generation with clear interfaces.
Select and compare embeddings using MTEB and BEIR, then apply BGE, GTE, and E5 models for domain tasks.
Implement dense, sparse, and hybrid retrieval, including BM25 plus vector search for higher recall and precision.
Use rerankers effectively, from cross-encoders to lightweight scorers, to improve groundedness and reduce hallucinations.
Choose and tune vector indexes with FAISS, and integrate Qdrant, Weaviate, Elasticsearch, or Milvus for production search.
Build multimodal RAG with CLIP, SigLIP, and ColPali for document and image retrieval in healthcare and retail scenarios.
Serve at scale using Text Generation Inference, vLLM, and Text Embeddings Inference, with batching and streaming.
Fine-tune with PEFT and LoRA, align with TRL, and train at scale with Accelerate while controlling cost.
Measure relevance and groundedness using Ragas, TruLens, and DeepEval, and create realistic evaluation sets.
Apply enterprise security, safetensors, role-based access, data residency, and audit trails in regulated environments.
Document responsibly with model and dataset cards, plus reproducible workflows using the Hub and Datasets.
Optimize for latency, throughput, and spend with caching, quantization, and observability patterns.
Code contentThis is a code-forward guide. You will find working Python, Bash, and SQL snippets that show each step, from indexing with FAISS and calling rerankers to serving models with TGI and vLLM, so you can move from theory into real projects quickly.
Why this book stands out
End-to-end coverage across retrieval, serving, evaluation, and governance, aligned to finance, healthcare, retail, and manufacturing.
Multimodal retrieval patterns with CLIP, SigLIP, and ColPali, not just text-only search.
Production focus using Inference Endpoints, Text Embeddings Inference, Text Generation Inference, and vLLM with practical SLAs.
Thorough evaluation and documentation practices, including groundedness checks and model or dataset cards tied to real deployments.
Grab your copy today to build RAG systems that are accurate, observable, and ready for enterprise scale.
