LLM Engineering with Python: Build, Test, Secure, and Deploy Production-Ready LLM Applications with Python, RAG, and AI Agents (en Inglés)
Reseña del libro "LLM Engineering with Python: Build, Test, Secure, and Deploy Production-Ready LLM Applications with Python, RAG, and AI Agents (en Inglés)"
Build production-ready LLM applications—not just impressive demos. Includes a complete companion GitHub repository with the book. Get the source code, tests, prompts, evaluation datasets, documentation, and hands-on project implementations at github.com/masud-dot/llm-engineering-with-python. You can follow the book chapter by chapter, run the examples, inspect the tests, and build on the same engineering patterns used throughout the book. Large language models can produce impressive results in a prototype. Turning that prototype into a reliable application is a different engineering problem. LLM Engineering with Python takes you from working API calls to production-oriented systems that are designed to be tested, evaluated, secured, observed, and deployed. You will learn how to: Design reliable LLM application architectures with Python Work with model APIs, streaming, asynchronous requests, retries, timeouts, fallbacks, logging, and cost controls Engineer prompts, context budgets, memory, and structured outputs with Pydantic Build embeddings, semantic search, vector-store integrations, and retrieval pipelines Build production-oriented RAG systems with hybrid retrieval, reranking, query rewriting, filtering, citations, grounding, and abstention Implement tool calling with validated contracts, execution controls, allowlists, and safety boundaries Build bounded AI agents with planning, tool composition, memory, approval gates, and auditable execution Understand and integrate Model Context Protocol (MCP) for standardized tool access Test nondeterministic LLM applications and evaluate the quality of generated output Build regression, adversarial, and CI/CD quality gates Defend against prompt injection, data leakage, unsafe tool use, and other application-level risks Control latency and cost with caching, routing, budgets, and measurement Add structured logging, tracing, metrics, alerting, and privacy-aware observability Serve applications with FastAPI and package them for containerized deployment Learn by building real projects. The capstone section brings the engineering concepts together through practical applications, including an AI Document Assistant, AI Test-Case Generator, AI SQL Assistant, AI Research Agent, and a production service that assembles the components into a deployable system. This book also emphasizes something often missing from LLM tutorials: measurement and verification. You will learn to distinguish software testing from model-output evaluation, measure retrieval quality, validate security controls, track cost and latency, and build quality gates that can detect regressions. Who is this book for? This book is designed for Python developers, software engineers, QA and automation engineers, backend developers, AI/ML engineers, and technical professionals who want to move beyond LLM experimentation and understand how production-grade AI applications are engineered. You should be comfortable reading and writing Python. No previous experience building LLM applications or advanced machine-learning background is required. Stop treating the LLM as the entire application. Learn how to engineer the system around it.
