Foundations
Naive RAG
The essentials, done well. Find relevant knowledge with vector search and turn it into straightforward, grounded answers.
BUILT FORSimple FAQs & first prototypes
Low complexityLow cost
BIG IDEAS. GROUNDED ANSWERS.
Your knowledge.
Your unfair
advantage.
Your data has the answers. Give it a voice.
Discover the right RAG architecture to turn business knowledge into business impact.
10 architectures to exploreFree sample playground
RAG, IN MOTION
ILLUSTRATED FLOW01
Big documents. Small, useful pieces.
Split your documents into chunks, keeping the source attached to each piece.
Anatomy of a grounded answerExample data · No live inference
REAL-WORLD USE CASES.
Customer support
Enterprise search
Knowledge management
Research & insights
THE ARCHITECTURE COLLECTION
Different challenges.
The right intelligence.
There’s no one-size-fits-all RAG.
Find the approach that fits your data, your goals,
and the way your business works.
Foundations
Conversational RAG
An assistant that keeps up. Uses conversation history to understand follow-up questions and retrieve the right context.
BUILT FORCustomer support & document chat
Medium complexityLow–medium cost
Versatile choice
Hybrid RAG
The best of both searches. Combines keyword precision with semantic understanding to find what really matters.
BUILT FORTechnical docs & enterprise search
Medium complexityMedium cost
Precision
Reranked RAG
A second look makes a difference. Retrieves broadly, then reorders results to put the most relevant evidence first.
BUILT FORHigh-relevance search experiences
Medium complexityMedium cost
Precision
Multi-query RAG
Go beyond the first interpretation. Searches multiple versions of a question to discover a wider range of useful evidence.
BUILT FORAmbiguous & complex questions
Medium complexityMedium–high cost
Precision
Parent-child RAG
Precision without losing the story. Finds small, relevant chunks and brings back the larger sections they belong to.
BUILT FORLong reports & detailed manuals
Medium complexityMedium cost
Built around your data. Chosen around your needs.Complexity & cost are relative.
UNDER THE INTELLIGENCE
Better context.
Better answers.
RAG stands for Retrieval-Augmented Generation. It connects an AI model to relevant information from your documents before it answers.
Your knowledge becomes the context. The AI makes it useful.
Explore the fundamentals on AWS01
Bring your knowledge
Start with the documents, help articles, and information your business already owns.
02
Find what matters
Your chosen architecture retrieves relevant evidence, so answers start with useful context.
Grounded in your sources
03
Put answers to work
Help people get clear answers with supporting sources. Review quality as your knowledge grows.
One idea. Ten ways to make it work for your business.RAG improves grounding; it does not guarantee accuracy.
BUILT FOR BUSINESS IMPACT
Less busywork.
More forward.
Turn time spent looking for answers into time spent moving your business ahead.
Support that scales with you
Give your team a head start on repetitive questions, grounded in your support knowledge.
Knowledge that gets to work
Make information across manuals, policies, and projects easier to find and use.
Start focused. Grow intentionally.
Prove value in one workflow, measure results, and expand when the numbers make sense.
Make the business caseESTIMATOR
Potential monthly capacity value$4,996
Illustration, not guaranteed savings or profit. Team × hours × 4.33 weeks × hourly cost, less your monthly RAG cost. Time saved is an assumption you control; setup costs are excluded.
THE DIRECTION IS CLEAR81%
of leaders surveyed in 2025 expected AI agents to be integrated into their AI strategy within 12–18 months.
Microsoft · 2025 Work Trend IndexFROM EXPERIMENT TO EVERYDAYAI is moving fast.
AI is moving fast.
Make your knowledge
part of the story.
As businesses adopt AI assistants, relevant company knowledge becomes more useful. RAG is one way to connect the two.
The survey measures expectations for AI agents, not RAG market growth. GOOD QUESTIONS. CLEAR ANSWERS.
A little more
context.
01Is RAG a model or an architecture?
RAG is an architecture that combines retrieval with a language model. The ten approaches here change how information is found, refined, or connected. They are not ten separate foundation models.
02Which architecture should I start with?
Start with the simplest approach that meets your requirements. Naive RAG is useful for focused FAQs, conversational RAG for follow-up questions, and hybrid RAG for exact terms alongside semantic search. Use our fit finder, then evaluate with your actual documents.
03Can I try it before committing?
Yes. Create a free account to search our Ragstack guides or upload your own PDF, DOCX, or DOC files up to 1 MB each. Preview extracted text and explore matching source passages. The playground demonstrates document retrieval and architecture flows; live AI generation is not connected yet.
04Will RAG guarantee accurate answers or lower costs?
No. Relevant sources can improve grounding, but retrieval can miss evidence and models can still make mistakes. Accuracy, latency, and cost depend on your data, architecture, and model. Evaluate representative questions and measure results before expanding.
05Can I switch architectures as my business grows?
Yes, architectures can evolve or be combined. A simple retrieval system can add reranking or hybrid search later. Some changes, such as adding a graph or parent-child chunks, require rebuilding parts of your index and retrieval pipeline.
Let’s put it to work.
The right architecture. Your knowledge. A whole new possibility.
Explore for free. Find your fit. Build with confidence.