Ingestion design
Parsers, chunking and metadata strategies per document type — with the trade-offs explained.
Popular Resource
RAG Implementation Guide
From chunking and retrieval to evaluation and ACL — a complete guide to production RAG.
- 8chapters from ingestion to ops
- 25+hard-won implementation lessons
- 1eval framework you can copy
Inside the guide
What you'll learn to build
Building production-ready retrieval-augmented generation systems.
Retrieval quality
Hybrid search, reranking and query rewriting that lift accuracy where it counts.
Evaluation methods
Golden sets, retrieval metrics and answer scoring wired into CI.
Operations and cost
Freshness, monitoring and token economics for production RAG.
What's inside
The guide we wish we'd had
Most RAG content stops at the happy path. This guide starts where demos break: messy documents, permission sprawl and accuracy plateaus.
Foundation
Ingestion decisions that determine your ceiling
Why chunking strategy matters more than model choice, how to handle tables and scans, and the metadata design that makes filtering and permissions possible later.
- Chunking strategies by content type, with numbers
- Table, scan and layout handling patterns
- Metadata schemas that age well
Ingest→
Chunk→
Embed→
Index→
Retrieve
PDFConfluenceSharePointSQLTicketsEmail
Grounded answer…with citations [1] [2] and permissions applied
Accuracy
From 60% demo to 90%+ production
The diagnostic sequence for disappointing RAG: measure retrieval first, fix search before prompts, add reranking where evidence supports it — each step with eval methodology included.
- Retrieval-first debugging sequence
- Hybrid search and reranking decision guides
- The accuracy plateau checklist
Groundedness
96%
Safety
100%
Task success
93%
Tone & format
91%
CI gate passedrelease promoted to production
Production
Permissions, freshness and the bill
ACL-aware retrieval architecture, incremental sync patterns and the cost levers that keep RAG affordable at scale — the chapters most guides skip entirely.
- Query-time permission enforcement patterns
- Freshness architecture with staleness alerts
- Token economics and caching strategies
✓PII detected & redactedenforced
✓Answer grounded in sourcespassed
✓Role-based data accessenforced
✕Out-of-scope tool callblocked
SOC 2GDPRAudit logHITL
Who it's for
Built for practitioners
Engineers building RAG
Concrete patterns with trade-offs, not tutorials.
Teams with disappointing RAG
The diagnostic path from mediocre to trusted.
Architects
Reference architecture with security designed in.
Eval owners
A complete quality framework to copy.
Product managers
What to demand from a RAG roadmap.
Security reviewers
The permission model to insist on.
Delivery
Learn, then apply
Practical guides and tools from teams who ship production AI — so your next decision is grounded in real delivery experience.
- Actionable playbooks
- Architecture blueprints
- Cost & ROI frameworks
- Security checklists
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1
Assess
Understand readiness, risks and opportunities.
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2
Learn
Use playbooks, guides and architecture patterns.
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3
Decide
Prioritize the right build path.
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4
Execute
Partner with Skugal to ship confidently.
FAQ
Common questions
Does it cover specific vector databases?
It covers selection criteria and patterns across Pinecone, pgvector, Elasticsearch and others — principles first, so the guide outlives any vendor's release cycle.
Is code included?
Reference snippets and eval harness examples, yes — enough to implement, not a framework to adopt.
We already have RAG in production. Still useful?
The accuracy-plateau and operations chapters were written for exactly you.
Next step
Get the guide
Build RAG that survives contact with your real documents.