Neo4j clustering configuration for high-availability GraphRAG
Entity resolution pipeline design and chunking strategies
Evaluation framework for graph retrieval quality
Production runbook: caching, monitoring, and scaling
Cypher query optimization patterns for LLM-generated queries
Sovereign AI deployment: running GraphRAG with local LLM inference on DGX Spark
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The information, code snippets, configuration files, and instructions provided in this product are shared for educational and informational purposes only. While every effort has been made to ensure accuracy, you are solely responsible for reviewing, testing, and adapting any code or configurations to your own environment before using them in production.
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From Demo to Production: The Gap Nobody Talks About
You built a GraphRAG prototype. It answered three hand-picked questions perfectly. Your CTO saw the demo and said "ship it."
Now you're staring at a list of questions nobody prepared you for:
How do you cache embeddings without serving stale results?
What's the latency budget for a graph traversal vs a vector lookup?
How do you A/B test a retrieval pipeline change without corrupting the production index?
What happens to your Neo4j connection pool at 500 concurrent requests?
How do you deploy a new embedding model without taking the graph offline?
How do you explain to your CTO why retrieval recall dropped from 92% to 87% last Tuesday?
This playbook answers every one of those questions — and the fifty more you haven't thought of yet. It's based on real production deployments, not blog posts.
The Problem
You've built a RAG prototype that works on three example questions. Now you need to ship it. You're staring down Neo4j clustering decisions, GPU vs. CPU serving tradeoffs, caching strategies you've never designed, and a CTO who wants to know the latency budget. Every blog post covers the happy path — none of them tell you what breaks at 10K QPS or how to debug a retrieval pipeline when recall drops overnight.
In 2026, there's a new dimension: sovereignty. The EU AI Act and Germany's Digital Sovereignty Act create regulatory pressure to keep LLM inference local. If your GraphRAG pipeline sends queries to a cloud API, your graph data — entities, relationships, business logic — all leave your network. This playbook now covers sovereign AI deployment as a first-class architecture pattern.
What This Guide Does For You
After reading this guide, you'll have the battle-tested architecture decisions, deployment patterns, and operational playbook to take a GraphRAG system from prototype to production — and the confidence to defend every tradeoff you make. This is what works after shipping GraphRAG under real traffic.
What You'll Be Able To Do
Make architecture decisions with conviction — know exactly when to use vector search, graph traversal, or both for different query types
Design production infrastructure — Neo4j clustering, Qdrant/Pinecone selection, GPU vs CPU serving tradeoffs
Decompose complex queries reliably — break questions into sub-queries, generate Cypher at runtime, resolve entities
Evaluate retrieval quality like a pro — MRR, NDCG, LLM-as-judge pipelines, A/B testing for RAG
Design a multi-level cache architecture — embedding, query, and result caches with invalidation policies
Observe everything in production — OpenTelemetry traces, structured logging, Grafana dashboards for latency/quality/cost
Optimize costs without sacrificing quality — embedding budget allocation, model tiering, cache hit ratio targets
Deploy sovereign GraphRAG — run your LLM locally on DGX Spark (single or dual-node) so graph data never leaves your network
Ship schema changes without downtime — CI/CD for graph schemas, blue/green embedding model deploys, migration strategies
Who Will Benefit Most
Senior engineers and architects who already understand RAG basics and need to build production-grade GraphRAG systems. This is not an introduction — it's a battle-tested playbook for going live and staying live.
What Success Looks Like
You'll be the person on your team who knows how to ship GraphRAG — not just demo it. Production architecture decisions, operational runbooks, cost projections, and a system that stays up under real traffic. That's the difference between a prototype and a product, and this playbook is the bridge.
Format & Delivery
9 chapters, 70+ pages (updated v1.4.0)
Code examples in Python, Cypher, and TypeScript
Architecture diagrams and decision trees
Sovereign AI deployment chapter with DGX Spark configuration
Immediate digital download (PDF)
Lifetime updates included
Before You Buy
The query router decision tree from Chapter 1 is available as a free preview →. If the decision tree and hybrid retrieval patterns make sense for your pipeline, the full guide delivers the rest of the system.
If you're still deciding, scroll back up to the benchmark table. 89% vs 62% on multi-hop questions — that's the number that matters.
