Built from a real application
The examples come from the companion angular-rag-demo, so every section stays grounded in UI behavior and implementation choices.
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Private AI Search & RAG Tutorial
A 90-Page Practical Architecture and Implementation Guide
Learn how a practical RAG system comes together from chunking and retrieval to context assembly, grounded answers, and evaluation, using a real application instead of abstract slides.
RAG as a full system: ingest, retrieval, context assembly, and grounded answers
Corpus design and chunking strategies, including practical trade-offs
Retrieval stack composition with vector search, BM25 baseline, and RRF fusion
Practical instead of hype-driven
The guide focuses on ranking quality, token budgets, evaluation, and trade-offs rather than one-off prompting tricks.
Useful for architecture decisions
It helps senior engineers and architects decide which retrieval layers are worth their complexity before shipping to production.
What You’ll Learn
RAG as a full system: ingest, retrieval, context assembly, and grounded answers
Corpus design and chunking strategies, including practical trade-offs
Retrieval stack composition with vector search, BM25 baseline, and RRF fusion
Knowledge graph expansion and cross-encoder reranking for quality gains
Context-window engineering with token budgeting and prompt context design
Evaluation workflow and production defaults for reliable deployment
Inside The Guide
Ch. 0-3
Foundations and system design
Start with privacy, threat modeling, and the full RAG pipeline so retrieval quality is framed as a systems problem from day one.
Ch. 4-9
Retrieval quality and ranking
Work through embedding space intuition, vector search, BM25, reciprocal rank fusion, graph expansion, and reranking with practical examples.
Ch. 10-13
Generation, evaluation, and defaults
Finish with context engineering, grounded answer generation, evaluation metrics, and production-ready defaults you can reuse in your own stack.
Screenshots From The Tutorial
Grounded answers in the Search tab
See how retrieval context, prompt constraints, and the final grounded answer fit together in one view.
Embedding neighborhoods made visible
The latent-space view makes chunk similarity and query proximity easier to reason about than raw vectors alone.
Compare retrieval strategies side by side
Vector, BM25, graph expansion, and fusion results are shown together so trade-offs stay concrete.
Knowledge graph expansion for retrieval
The guide explains when graph signals improve recall and when they simply add noise to the pipeline.
Reranking that changes the final answer
Follow rank movements and score shifts to understand where cross-encoder reranking actually earns its cost.
Token budgeting and context assembly
Context-window planning is treated as an engineering system, not a prompt-writing afterthought.
About This Ebook
A practical 90-page guide to designing private AI search and retrieval-augmented generation systems in TypeScript.
Instead of isolated tricks, the book explains the complete retrieval pipeline from corpus and chunking decisions to ranking quality, context assembly, grounded answers, and evaluation.
Each chapter connects concept, trade-off, and implementation detail using concrete examples and screenshots from the angular-rag-demo application.
Who It’s For
Built for engineering teams responsible for enterprise AI outcomes: senior developers implementing RAG services, solution architects defining system boundaries, and AI platform leads standardizing quality and governance. If your team must deliver trustworthy internal copilots with measurable retrieval quality, controlled cost, and production-ready safety defaults, this guide is designed for you.
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