Layer 1
Knowledge sources
Authoritative documents, records, webpages, databases, tickets, policies, and archives.
Ownershipfreshnesspermissionsformat qualitysource authority
Educational reference hub
The practical portal for Retrieval-Augmented Generation
Learn what RAG is, compare tools, design architectures, evaluate quality, and build grounded AI systems that users can trust.
RAG connects language models with external knowledge sources to improve grounding, accuracy, traceability, and domain relevance.
23
Tool profiles
Structured entries with verification notes and official links.
9
Architecture patterns
From basic RAG to enterprise observability and Graph RAG.
6
Comparison pages
Criteria-based, cautious, vendor-neutral guidance.
6
Audience pathways
Entry points for beginners, engineers, educators, and decision-makers.
RAG pipeline
01 · Knowledge
Documents
02 · Prepare
Ingestion
03 · Prepare
Chunking
04 · Index
Embeddings
05 · Index
Vector DB
06 · Find
Retrieval
07 · Refine
Reranking
08 · Assemble
Context
09 · Answer
Generation
10 · Trust
Citations
Flow logic
RAG turns raw knowledge into searchable evidence, selects the most relevant context, then generates an answer that users can verify through citations and evaluation.
Ground answers in sources
Refresh knowledge without retraining
Expose citations and evidence
Measure quality over time
Portal finder
Find the right RAG topic fast
What is RAG?
conceptDefinition, purpose, and the grounding role of Retrieval-Augmented Generation.
RAG vs fine-tuning
conceptWhen to retrieve knowledge and when to adapt model behavior.
Tools directory
directorySearch RAG platforms, orchestration frameworks, vector databases, evaluation tools, and document processing tools.
Dify guide
guideHow Dify fits into RAG application building, workflows, knowledge bases, and agents.
Architectures
referenceBasic, advanced, hybrid, metadata-aware, graph, agentic, multimodal, local, and enterprise RAG patterns.
Implementation playbook
playbookA practical lifecycle for planning, building, testing, launching, and maintaining RAG systems.
Evaluation center
playbookHow to evaluate retrieval, answer faithfulness, citations, latency, safety, and business outcomes.
Glossary
referenceSearchable definitions for embeddings, vector databases, reranking, grounding, Graph RAG, and more.
Choose your pathway
Different users need different levels of depth. Start with the pathway closest to your role, then move into architectures, tools, and evaluation.
Explore the portal
Use the hub as a map: learn the concepts, choose a stack, build a pipeline, evaluate quality, and keep knowledge current.
Learn RAG
Open the learn rag section.
RAG Tools
Open the rag tools section.
Dify Guide
Open the dify guide section.
Architectures
Open the architectures section.
Tutorials
Open the tutorials section.
Use Cases
Open the use cases section.
Glossary
Open the glossary section.
Comparisons
Open the comparisons section.
Resources
Open the resources section.
Implementation
Open the implementation section.
Evaluation
Open the evaluation section.
The RAG stack, end to end
Effective RAG is not a single tool. It is a chain of editorial, retrieval, generation, evaluation, and operational decisions.
Layer 2
Ingestion and parsing
Convert files into structured text, tables, page references, images, and metadata.
OCR qualitylayout handlingdeduplicationlanguage detectionupdate cadence
Layer 3
Chunking and metadata
Create retrievable units that preserve meaning, context, and source traceability.
Chunk sizeoverlapsection boundariespage numbersaccess labels
Layer 4
Embeddings and indexing
Represent chunks for semantic retrieval and store them with searchable metadata.
Embedding modelvector databasehybrid indexingnamespace strategyre-indexing
Layer 5
Retrieval and reranking
Find relevant evidence, filter it, and order the best context for generation.
Top-khybrid weightingmetadata filtersrerankerquery rewriting
Layer 6
Generation and citations
Assemble prompts, instruct the model, generate answers, and cite source passages.
Prompt policysource displayunsupported-answer behaviormodel choicetone
Layer 7
Evaluation and operations
Measure quality, detect regressions, monitor traces, and improve the system over time.
Golden questionsfaithfulness checkshuman reviewtelemetryfeedback loops
Why RAG matters
RAG is one of the most practical patterns for building AI systems that need current, private, or specialized knowledge.
Better grounding
Private and domain knowledge
Reduced hallucination
Updatable knowledge
Explainability through sources
Risks every RAG project must control
A serious RAG portal should teach the failure modes, not only the happy path.
Permission leakage
Apply document-level access controls before retrieval, not only after generation.
Stale knowledge
Track last-updated metadata and create refresh workflows for high-authority sources.
Prompt injection
Treat retrieved text as untrusted content and keep system instructions separate.
Unsupported answers
Require citation-backed claims and define a no-answer policy.
Bad OCR or parsing
Sample parsed output visually and preserve source page references.
Overconfident automation
Use human review for legal, medical, financial, policy, or high-impact decisions.
Who is this site for?
The hub is written for both beginners and experts, with careful explanations, structured comparisons, and practical architecture guidance.
Developers
Build reliable retrieval systems and choose maintainable toolchains.
Researchers
Compare RAG patterns, evaluation methods, and retrieval tradeoffs.
Educators
Teach grounding, sources, and practical AI literacy.
Librarians and documentalists
Connect metadata, archives, and documentary authority to AI interfaces.
Businesses
Assess platforms, governance, and production readiness.
Public institutions
Design traceable knowledge services with source accountability.