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Building Useful AI with RAG: Learn the Core RAG Workflow by Building an AI Assistant That Answers from Trusted Knowledge - Brossura
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Build AI that answers from the information you trust, not just from what a language model already knows.
Large language models are powerful, but they do not automatically know your private documents, internal knowledge, product information, policies, research, or other specialized sources. Retrieval-Augmented Generation (RAG) solves that problem by connecting language models with relevant external knowledge before an answer is generated.
Building Useful AI with RAG is a practical, beginner-friendly guide to understanding that workflow by building it yourself.
Instead of overwhelming you with multiple frameworks, databases, and advanced techniques at once, this book focuses on the core RAG process and develops one continuous project: a Trusted Knowledge Assistant that can search a collection of documents, retrieve relevant information, and use that evidence to produce grounded answers.
You will move step by step from raw documents to a complete working RAG application while understanding what each component contributes and how the pieces fit together.
Inside the book, you will learn how to:Understand why language models need external knowledge and where RAG fits into modern AI applications
Prepare and normalize trusted documents for reliable retrieval
Divide documents into useful chunks without losing important context
Understand and create embeddings for documents and user questions
Build a searchable vector index
Use semantic similarity to retrieve the most relevant knowledge
Preserve metadata so retrieved information remains connected to its original source
Build reusable retrieval logic for an AI assistant
Combine retrieved knowledge with a language model to create an end-to-end RAG pipeline
Generate answers that stay grounded in the available evidence
Display sources and handle questions the knowledge base cannot answer
Evaluate retrieval quality, relevance, groundedness, and answer completeness
Diagnose weak RAG results and improve chunking, queries, metadata filtering, and retrieval settings
Understand when hybrid search and reranking can improve results
Build a simple question-and-answer interface for the completed assistant
Update and reindex the knowledge base as your documents change
This book emphasizes understanding over copying code. You will build simple versions first, inspect how they behave, identify their limitations, and improve them deliberately. The goal is to help you understand the reasoning behind RAG well enough to adapt the workflow to your own projects instead of depending on a single framework or tool.
Whether you want to build an AI assistant for company documents, product manuals, policies, technical documentation, research material, course content, or another controlled knowledge source, the same foundational workflow applies.
By the end of the book, you will understand how information moves from trusted documents to retrieval, context, and finally a grounded AI answer, and you will have built the complete workflow yourself.
If you are comfortable with basic Python and want to move from experimenting with language models to building useful AI applications that can answer from real knowledge, this book gives you a clear place to start.
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Building Useful AI with RAG (Paperback)
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Paperback. Condizione: new. Paperback. Build AI that answers from the information you trust, not just from what a language model already knows.Large language models are powerful, but they do not automatically know your private documents, internal knowledge, product information, policies, research, or other specialized sources. Retrieval-Augmented Generation (RAG) solves that problem by connecting language models with relevant external knowledge before an answer is generated.Building Useful AI with RAG is a practical, beginner-friendly guide to understanding that workflow by building it yourself.Instead of overwhelming you with multiple frameworks, databases, and advanced techniques at once, this book focuses on the core RAG process and develops one continuous project: a Trusted Knowledge Assistant that can search a collection of documents, retrieve relevant information, and use that evidence to produce grounded answers.You will move step by step from raw documents to a complete working RAG application while understanding what each component contributes and how the pieces fit together.Inside the book, you will learn how to: Understand why language models need external knowledge and where RAG fits into modern AI applicationsPrepare and normalize trusted documents for reliable retrievalDivide documents into useful chunks without losing important contextUnderstand and create embeddings for documents and user questionsBuild a searchable vector indexUse semantic similarity to retrieve the most relevant knowledgePreserve metadata so retrieved information remains connected to its original sourceBuild reusable retrieval logic for an AI assistantCombine retrieved knowledge with a language model to create an end-to-end RAG pipelineGenerate answers that stay grounded in the available evidenceDisplay sources and handle questions the knowledge base cannot answerEvaluate retrieval quality, relevance, groundedness, and answer completenessDiagnose weak RAG results and improve chunking, queries, metadata filtering, and retrieval settingsUnderstand when hybrid search and reranking can improve resultsBuild a simple question-and-answer interface for the completed assistantUpdate and reindex the knowledge base as your documents changeThis book emphasizes understanding over copying code. You will build simple versions first, inspect how they behave, identify their limitations, and improve them deliberately. The goal is to help you understand the reasoning behind RAG well enough to adapt the workflow to your own projects instead of depending on a single framework or tool.Whether you want to build an AI assistant for company documents, product manuals, policies, technical documentation, research material, course content, or another controlled knowledge source, the same foundational workflow applies.By the end of the book, you will understand how information moves from trusted documents to retrieval, context, and finally a grounded AI answer, and you will have built the complete workflow yourself.If you are comfortable with basic Python and want to move from experimenting with language models to building useful AI applications that can answer from real knowledge, this book gives you a clear place to start. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Codice articolo 9798170877591
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Building Useful AI with RAG: Learn the Core RAG Workflow by Building an AI Assistant That Answers from Trusted Knowledge
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Building Useful AI with RAG (Paperback)
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Paperback. Condizione: new. Paperback. Build AI that answers from the information you trust, not just from what a language model already knows.Large language models are powerful, but they do not automatically know your private documents, internal knowledge, product information, policies, research, or other specialized sources. Retrieval-Augmented Generation (RAG) solves that problem by connecting language models with relevant external knowledge before an answer is generated.Building Useful AI with RAG is a practical, beginner-friendly guide to understanding that workflow by building it yourself.Instead of overwhelming you with multiple frameworks, databases, and advanced techniques at once, this book focuses on the core RAG process and develops one continuous project: a Trusted Knowledge Assistant that can search a collection of documents, retrieve relevant information, and use that evidence to produce grounded answers.You will move step by step from raw documents to a complete working RAG application while understanding what each component contributes and how the pieces fit together.Inside the book, you will learn how to: Understand why language models need external knowledge and where RAG fits into modern AI applicationsPrepare and normalize trusted documents for reliable retrievalDivide documents into useful chunks without losing important contextUnderstand and create embeddings for documents and user questionsBuild a searchable vector indexUse semantic similarity to retrieve the most relevant knowledgePreserve metadata so retrieved information remains connected to its original sourceBuild reusable retrieval logic for an AI assistantCombine retrieved knowledge with a language model to create an end-to-end RAG pipelineGenerate answers that stay grounded in the available evidenceDisplay sources and handle questions the knowledge base cannot answerEvaluate retrieval quality, relevance, groundedness, and answer completenessDiagnose weak RAG results and improve chunking, queries, metadata filtering, and retrieval settingsUnderstand when hybrid search and reranking can improve resultsBuild a simple question-and-answer interface for the completed assistantUpdate and reindex the knowledge base as your documents changeThis book emphasizes understanding over copying code. You will build simple versions first, inspect how they behave, identify their limitations, and improve them deliberately. The goal is to help you understand the reasoning behind RAG well enough to adapt the workflow to your own projects instead of depending on a single framework or tool.Whether you want to build an AI assistant for company documents, product manuals, policies, technical documentation, research material, course content, or another controlled knowledge source, the same foundational workflow applies.By the end of the book, you will understand how information moves from trusted documents to retrieval, context, and finally a grounded AI answer, and you will have built the complete workflow yourself.If you are comfortable with basic Python and want to move from experimenting with language models to building useful AI applications that can answer from real knowledge, this book gives you a clear place to start. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Codice articolo 9798170877591
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