RAG Architect — The Definitive Playbook for Production Retrieval Systems
🔥 The definitive playbook for shipping production RAG systemsEverything you need to design, build, ship, evaluate, and operate a retrieval-augmented generation system that survives real users — compiled from production systems shipped between 2023 and 2026 and post-mortem interviews with teams operating RAG at scale.Not another intro tutorial. Not another "hello, LangChain" blog post. This is the architect-tier resource senior engineers, tech leads, and AI consultants use to make design decisions.✅ What's inside 📘 RAG Architect Playbook (PDF) — a dense, print-ready technical book covering foundations, 12 architecture patterns, evaluation, cost, and operations 🗺 12 print-ready architecture diagrams — Naive, Production, Agentic, GraphRAG, Multimodal, Voice, Chat-with-Docs, Code, Self-RAG, CRAG, Hybrid RRF, Eval Pipeline ⚡ 6 Python code starters (MIT) — naive_rag, production_rag (hybrid + rerank + citations), chunking_strategies (6 strategies), hybrid_search (RRF), eval_pipeline (RAGAS-style) ✍️ 40 production prompt templates — query rewrite, HyDE, decomposition, grounded answering, faithfulness judge, hallucination detector, jailbreak detector, and more 📊 Interactive vendor matrix (HTML) — Pinecone vs Weaviate vs Qdrant vs pgvector vs Chroma vs Milvus vs Vespa vs OpenSearch vs LanceDB vs Turbopuffer 💰 Interactive cost calculator (HTML) — plug in corpus size, QPS, model choices → get monthly bill breakdown and cost-per-query health check 🗂 Notion-importable bundle — executive summary, 40-item launch checklist, 12-failure incident runbook 💎 Why it's premium Naive tutorial RAG Architect Pages 5–15 40+ dense pages, 20 chapters Architectures 1 (naive) 12 patterns with tradeoff tables Code copy-paste snippet 6 production files, MIT license Prompts 2–3 40 categorized templates Evaluation rarely mentioned full RAGAS harness + judge prompts Vendor decisions opinion 10-vendor matrix + decision shortcut Cost economics ignored interactive calculator Operations ignored 40-item checklist + 12-failure runbook 💰 Real ROI framing Replaces reading 40+ scattered papers, blog posts, and vendor docs Cuts weeks of architecture-decision cycles down to hours Prevents the three failure modes that kill 90% of RAG projects (no eval harness · wrong chunking · retrieval that only works on demo queries) Every code file is battle-tested — drop in and ship 🎁 Bonuses ✅ Free lifetime updates — every future edition ✅ MIT-licensed code — use commercially, no attribution required ✅ 48-hour buyer support — reply to your receipt 🛠 How to use it Read the executive summary — one page, decides which architecture is right for you Read chapters on the 2–3 patterns that match — each has diagram, code pointer, tradeoff table, pitfalls Open the vendor matrix → pick a vector DB in 5 minutes Open the cost calculator → size your monthly bill before you commit Drop the code starters into your repo → build against production_rag.py, eval with eval_pipeline.py Run the launch checklist before you ship ❓ FAQBeginners or experienced engineers? Both — the executive summary makes it accessible; the depth serves architects making real design decisions.Which stacks does it cover? LLMs: OpenAI, Anthropic, Google, self-hosted Llama. Vector DBs: Pinecone, Weaviate, Qdrant, Chroma, pgvector, Milvus/Zilliz, Vespa, Elasticsearch/OpenSearch, LanceDB, Turbopuffer.Do I need Python? Code starters are Python, but concepts translate to any language.Refunds? See checkout page for current refund terms.Updates? Yes — every future edition, free of charge to prior buyers.🌍 Worldwide-compatibleDigital delivery to every country. English content. USD pricing. Works with any RAG stack.⚡ Click Get →Ship the naive version this week. Ship the production version next month. Revisit the eval chapter every time something feels off. Built with obsessive attention. Now go build the thing.
Get it → skjabedulhaque.gumroad.com