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RAG Explained for Builders: Ground AI Answers in Your Data
A detailed briefing on RAG types compared: vector RAG, hybrid, GraphRAG, Agentic RAG, vectorless, multimodal, and more, plus tools and how Compile Vision ships production document QA.
Every company has the same quiet problem: the answers live in PDFs, Notion pages, tickets, and shared drives, while staff still ask chatbots that have never read those files. Retrieval-augmented generation, or RAG, is the pattern that fixes that gap.
Instead of hoping a model "remembers" your policies, you search your documents first, then hand the best passages to the model. The model writes an answer grounded in that context, ideally with citations so a human can verify the claim in one click.
That is why RAG remains one of the most searched applied-AI topics. It is usually cheaper and safer than full fine-tuning for knowledge work, and it maps cleanly onto real products: support copilots, policy assistants, and internal search that finally feels intelligent.
How RAG works, in newspaper plain English
The pipeline has five acts. First, ingest: parse files and clean the text. Second, chunk: split long documents into passages with metadata such as source, page, and tenant. Third, embed and index: store vectors so similar meaning can be found quickly. Fourth, retrieve at question time: often hybrid keyword plus vector search, then a reranker. Fifth, generate: the model answers only from the retrieved passages and cites them.
The research foundation goes back to Lewis et al. (2020), who showed that pairing a retriever with a generator improves knowledge-intensive tasks. Modern stacks still follow that idea, with better chunking, hybrid search, and evaluation.
How you can build a RAG system
Begin with one corpus and one user: for example, HR policies for employees, or product manuals for support. Build an ingest job that can re-run when files change. Choose a chunk size that keeps paragraphs coherent. Index with access-control metadata from day one so Tenant A never retrieves Tenant B.
For the first demo, LangChain or LlamaIndex plus pgvector on Postgres is enough. Add a chat UI that always shows citations. Create twenty golden questions with expected source docs. Score groundedness and 'I do not know' behavior when the corpus has no answer.
Production adds the hard parts: scheduled re-indexing, PII redaction, rate limits, observability, and a path to escalate to a human. That is where many demos die, and where Compile Vision usually enters: we turn the notebook into a maintainable service.
Where RAG shines, and where it struggles
RAG shines for private knowledge Q&A, onboarding assistants, compliance lookup, and technical support over manuals. It struggles when documents are scanned badly, when answers require multi-hop reasoning across dozens of files without good retrieval, or when the business needs a fixed tone that only fine-tuning can lock in.
A practical rule: try RAG before fine-tuning for most knowledge products. Fine-tune later for style or structured output if the grounded answers are already correct. First, though, pick the RAG flavor that matches your data: vector, hybrid, graph, agentic, or vectorless.
Key takeaways
Retrieve first, then generate: that is the whole idea.
Vector RAG is the MVP; hybrid, GraphRAG, and Agentic RAG solve different failure modes.
Citations and tenant filters belong in the design from day one.
Try the simplest RAG type that fits before adding graphs or agents.
Compile Vision can build the full ingest-to-UI pipeline for you.
Types of RAG (and how they differ)
Classic vector RAG is the default. Other variants exist because documents are messy: some need keywords, some need relationships, some need an agent to decide what to fetch next.
Naive / vector RAG
- How it works
- Embed chunks, retrieve nearest neighbors by similarity, stuff them into the prompt, generate.
- Best for
- Clean prose docs, FAQs, handbooks, first production MVP.
- How it differs
- Fast to ship. Weak on exact IDs, tables, and multi-hop "how are A and B related?" questions.
Hybrid RAG (vector + keyword)
- How it works
- Combine dense vectors with BM25/full-text, then merge or rerank results.
- Best for
- Product SKUs, error codes, names, legal clause numbers, mixed corpora.
- How it differs
- Fixes many "semantic miss" failures. Slightly more ops than pure vector search.
GraphRAG
- How it works
- Build a knowledge graph (entities + relationships) from docs; retrieve subgraphs or community summaries for the answer.
- Best for
- Org charts, supply chains, research corpora, "who connects to what" questions.
- How it differs
- Excels at relationships and global themes. Heavier ingest and graph maintenance than chunk vectors.
Agentic RAG
- How it works
- An agent decides when to retrieve, what query to run, whether to retrieve again, and when to stop; may call multiple indexes or tools.
- Best for
- Multi-step questions, mixed tools (docs + CRM + web), follow-up chat.
- How it differs
- More accurate on hard queries; higher latency/cost; needs guardrails like any agent.
Vectorless RAG
- How it works
- Skip (or de-emphasize) embeddings: use keyword search, SQL, structured filters, full-doc context windows, or model-native file search.
- Best for
- Highly structured data, small corpora that fit the context window, strong keyword corpora.
- How it differs
- Simpler when vectors add little value. Can miss paraphrase matches that embeddings catch.
Hierarchical / parent-child RAG
- How it works
- Retrieve small chunks for precision, then expand to parent sections for fuller context.
- Best for
- Long manuals and books where a tiny chunk is incomplete.
- How it differs
- Better context than tiny chunks alone; needs careful parent/child indexing.
Corrective / Self-RAG style
- How it works
- Model or critic checks if retrieved docs are relevant; may rewrite the query or retrieve again before answering.
- Best for
- Noisy corpora where top-k often returns junk.
- How it differs
- Improves groundedness; adds round-trips and evaluation complexity.
Multimodal RAG
- How it works
- Index and retrieve images, charts, slides, or audio transcripts alongside text.
- Best for
- Design docs, scanned forms, slide decks, industrial manuals with figures.
- How it differs
- Needs multimodal embeddings or dual indexes; ingest is harder than text-only.
SQL / structured RAG
- How it works
- LLM turns the question into SQL or API filters over tables, then generates an explanation from rows.
- Best for
- Analytics, inventories, transactions, "how many / which SKU" questions.
- How it differs
- Grounded in rows, not paragraphs. Schema quality matters more than chunk size.
Streaming / real-time RAG
- How it works
- Index keeps updating from live feeds (tickets, chats, sensors) with short freshness SLAs.
- Best for
- Support desks, newsrooms, ops monitors.
- How it differs
- Freshness is the product. Stale indexes fail even if retrieval math is perfect.
Tools & stack options
Practical pieces teams use to prototype and ship.
pgvector / Pinecone / Weaviate
Vector index for semantic retrieval
Postgres + full-text search
Hybrid keyword retrieval and metadata filters
Neo4j / graph stores + GraphRAG tooling
Entity-relationship retrieval for GraphRAG
OpenAI / Cohere / local embedders
Turn text chunks into vectors
LangChain / LlamaIndex / LangGraph
Classic RAG pipelines and agentic retrieval loops
FastAPI microservice
Separate ingest, retrieve, and generate APIs
Unstructured / custom parsers
PDF, HTML, and office document extraction
Rerankers (Cohere / cross-encoders)
Improve top-passage quality before generation
LangSmith / custom evals
Groundedness, citation, and regression tests
What you can build
- Employee handbook and policy assistant with citations
- Customer support copilot over product manuals and tickets
- GraphRAG over org or research relationships
- Agentic RAG that also queries CRM or ticketing tools
- SQL RAG for inventory and analytics questions
- Multimodal RAG over slides, scans, and figures
Architecture & diagrams
Classic RAG architecture
Documents flow into an index; questions retrieve context before generation.
RAG type map
How common RAG variants relate, from simple vector search to graphs and agents.
Where quality usually breaks
A quick map of failure points teams should test early.
From demo to production
A realistic path from notebook prototype to a Compile Vision-style service.
References & further reading
- Retrieval-Augmented Generation (Lewis et al., 2020)
Foundational RAG paper combining retrieval with generation.
- Microsoft GraphRAG
Open-source GraphRAG approach for knowledge-graph-oriented retrieval.
- Compile Vision: production RAG pipelines
Our deeper production-oriented companion post.
- LangChain RAG docs
Practical orchestration patterns for RAG apps.
- LlamaIndex documentation
Indexing and query pipelines for document QA.
Need a RAG system on your company documents?
Contact Compile Vision. We help you choose the right RAG type (vector, hybrid, GraphRAG, agentic, and more), then build ingest, secure retrieval, cited answers, and the UI. Start at /contact or email contact@compilevision.com.
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Learning / build steps
- 1
Pick RAG type
Vector, hybrid, GraphRAG, agentic, SQL, or multimodal
- 2
Ingest
Parse PDFs/docs, clean text, attach source metadata
- 3
Index
Vectors, keywords, graph, or tables depending on type
- 4
Retrieve + generate
Fetch context, answer with citations, optionally loop
- 5
Observe
Track quality, staleness, cost, and failures
Ready when you are
Compile Vision turns these playbooks into production systems. Tell us your use case and we will map a delivery plan.
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