Building LLM Applications Without the Fog: A Practical Guide to RAG, Embeddings, Vector Databases, Fine-Tuning, Evaluation, and AI-Powered Apps (LLMs Without the Fog Book 2)
English | May 10, 2026 | ASIN: B0H1737GFT | 438 pages | Epub | 277.20 KB
Everyone is using AI. Far fewer people know how to build useful applications with it.Large Language Models can write, summarize, classify, code, and analyze. But building an AI-powered application that real users can depend on requires more than a prompt.A good LLM application is a system.It needs APIs, prompt templates, structured outputs, embeddings, vector databases, retrieval, evaluation, error handling, privacy awareness, and practical product design.Building LLM Applications Without the Fog is the second book in the LLMs Without the Fog series - a practical guide for developers, students, technical founders, data analysts, software engineers, and builders who want to move from understanding LLMs to creating real AI-powered applications.This is the builder book.Before you move into production AI, advanced agents, LLMOps, security, deployment, or an LLM engineering career, you need to understand how LLM applications are actually designed and built.Inside, you will learn:What it really means to build with LLMsWhy an LLM application is more than a chatbotHow an LLM-powered app is structuredHow software connects to model APIsHow prompt templates improve consistency and reuseHow structured outputs turn responses into usable dataWhat embeddings are and why they make meaning searchableHow vector databases support semantic searchWhat RAG is and why it mattersHow to connect LLMs to documents and knowledge basesWhy RAG reduces hallucination but does not eliminate itWhen to use prompting, RAG, fine-tuning, tools, or human reviewWhy evaluation matters more than impressive demosHow to recognize common LLM application failure modesHow to move from prototype to practical AI productThis book is not about training frontier models from scratch.It is about building intelligently with existing models.By the end, you will understand that useful AI applications are not created by simply "adding AI" to a product. They are designed around real users, real data, real workflows, and real limitations.Building LLM Applications Without the Fog continues the complete series:Book 1 - LLMs Without the FogBuild your foundation and understand Large Language Models clearly.Book 2 - Building LLM Applications Without the FogLearn how real LLM-powered applications are built with APIs, embeddings, vector databases, RAG, evaluation, and practical app design.Book 3 - Production LLMs Without the FogUnderstand how serious AI systems are deployed, secured, monitored, operated, and improved.Book 4 - Becoming an LLM Engineer Without the FogFollow a practical roadmap to build skills, create portfolio projects, and move toward opportunities in the AI field.If Book 1 helped you understand LLMs, this book helps you build with them.Move from understanding to building. Create useful LLM applications without the fog.
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