Retrieval-augmented generation is a way to make a language model answer from your own data rather than only its training. The system retrieves relevant material at query time and gives it to the model as context, so answers are grounded in current, specific sources instead of the model's memory.

What are the main types of RAG in 2026?

Five patterns cover most real systems: Hybrid RAG (combines meaning-based and keyword search), GraphRAG (retrieves over a knowledge graph of entities and relationships), Agentic RAG (an agent plans and uses multiple tools in a loop), Corrective RAG or CRAG (grades retrieval quality before answering), and Multimodal RAG (one index across text, images and tables).

Which RAG architecture should I use?

Start with the simplest pattern that clears your accuracy bar. Hybrid RAG is the sensible default for general document search. Use GraphRAG when answers depend on relationships between entities, Agentic RAG for complex multi-source questions, Corrective RAG where a wrong answer is costly, and Multimodal RAG when the answer lives in images or tables. Most mature systems combine several.

What is the difference between Hybrid RAG and Agentic RAG?

Hybrid RAG is a retrieval method — it fuses meaning-based (vector) and keyword (BM25) search into one result set. Agentic RAG is a control pattern — an agent decides which tools to call, including retrieval, and loops until it has enough to answer. You can use hybrid retrieval inside an agentic system; they operate at different levels.

Why do RAG projects fail?

Usually not because of the model, but because the retrieval architecture was chosen by default. Naive similarity search is applied to a problem that needed exact-match, relationship, multi-step, or table-and-image retrieval — so it looks fine in a demo and degrades on real data. Matching the architecture to the problem, with an evaluation harness to prove it, is what separates a demo from a system.

The five RAG architectures worth knowing in 2026 — and when to use each

“RAG” stopped being one thing a while ago. The word now covers at least five distinct architectures, and choosing the wrong one is where most retrieval projects quietly fail — not in the model, in the design.

Retrieval-augmented generation is how you get a language model to answer from your data instead of its training. The naive version — chunk the documents, embed them, search by similarity, hand the top matches to the model — is where everyone starts, and for a surprising number of problems it is the wrong tool. It searches by what sounds similar, which is exactly wrong when the answer depends on an exact code, a relationship between entities, a figure inside a table, or several steps of reasoning across systems. The interesting work in 2026 is not a better embedding model. It is knowing which of these five patterns a given problem actually needs — and being honest that the more capable ones cost more to run.

1. Hybrid RAG — meaning plus keywords

What it is. Run two searches at once. A dense (vector) search finds text by meaning; a sparse (keyword, BM25) search finds text by exact terms. The two ranked lists are fused into one, and the top results go to the model.

Why it matters. Pure vector search is good at matching “affordable airfare” to “cheap flights” and hopeless at matching a part number, an error code, a clause reference, or a rare surname. Keyword search is the reverse. Fusing them covers both blind spots, which is why hybrid is the sensible default for most document search.

Use case — an internal support assistant over technical documentation. An engineer asks “why does the gateway drop connections under load” and also pastes “ERR_5521”. The meaning search finds the troubleshooting narrative; the keyword search pins the exact error code and the config flag named in one line of the manual. Neither alone answers it well; together they do.

2. GraphRAG — the answer lives in the relationships

What it is. Instead of storing loose text, you extract the entities — people, companies, places, projects — and the relationships between them into a knowledge graph, then retrieve the relevant slice of that graph, plus summaries of each cluster, to answer.

Why it matters. Some questions are not answerable from any single passage, because the answer is spread across many and only exists in the connections. No paragraph says “these three suppliers are all linked to one flagged parent company” — that fact lives in the edges, not the text.

Use case — supplier and counterparty due diligence. Before signing, a firm asks “is this new vendor connected, through directors or ownership, to any entity we have already flagged”. A graph of companies, directors and ownership links answers a multi-hop question that similarity search cannot see. It is heavier to build and maintain, so it earns its place only when the value genuinely sits in the relationships.

3. Agentic RAG — retrieval becomes a plan, not a step

What it is. A planning agent decides how to answer. It chooses among tools — vector search, web search, a SQL database — runs them, checks whether it has enough, and loops until it is confident, then a reasoning step writes the answer.

Why it matters. Real questions often need several sources and several steps. “Compare our position against the latest market data” means querying an internal database and searching the web, then reasoning over both. One-shot retrieval cannot do that. This is also the pattern where cost discipline matters most: the loop multiplies model calls, so it needs an iteration cap and a cost-per-task budget or it runs away with itself.

Use case — a tender-intelligence agent. We built and run one in production for Magellan Circle, an EU public-affairs and funding advisory. It ingests tenders from multiple public sources daily, retrieves against the client’s own material, extracts structured criteria, and drafts fully-cited first-pass bid responses — compressing weeks of manual research into hours. Retrieval there is not a single lookup; it is a plan the agent executes and revises.

4. Corrective RAG (CRAG) — grade the retrieval before you trust it

What it is. Add a quality check after retrieval. An evaluator grades what came back: if it is good, answer; if it is ambiguous, rewrite the query and try again; if it is bad, fall back to another source such as web search before answering.

Why it matters. Ordinary RAG trusts whatever it retrieved. When the retrieval is poor, the model still answers — confidently, from weak material. That is the mechanism behind a whole class of hallucinations. CRAG catches bad retrieval before it reaches the answer, at the cost of an extra step.

Use case — a customer-facing policy and compliance desk. A support assistant answers questions about entitlements, cover, or regulated terms. A wrong answer here is not a bad demo, it is a liability. The grader refuses to answer from thin retrieval — it re-queries or escalates instead of guessing — so “we are not sure, here is who to ask” replaces a confident, incorrect reply.

5. Multimodal RAG — one index across text, images and tables

What it is. Retrieval that is not limited to text. Text, images, charts and tables are embedded into one shared index, so a query can surface the diagram or the table that actually holds the answer, and a vision-capable model reads it.

Why it matters. Real documents are not clean prose. Financial reports keep the number in a table, engineering manuals put the answer in a diagram, product data lives in a spec grid. Text-only RAG is blind to all of it.

Use case — reading figures out of financial and technical documents. “What was the reported figure in the segment table on page 40” is unanswerable by text search, because the number lives in a table, not a sentence. A multimodal index retrieves the table itself and lets the model read across rows and columns. It is the priciest pattern to run, so it is worth it only when the source material is genuinely visual.

Our point of view

These five are not rivals to pick between once. They stack. A serious system is often hybrid retrieval, wrapped in an agentic loop, with a corrective grading step, over an index that happens to be multimodal. Each layer adds capability and cost, so the discipline is to add a layer only when a real limitation forces it — not because a diagram made it look mandatory.

At Agent Foundry Labs we start from the question, not the architecture. Most business problems do not need the most elaborate pattern; they need the simplest one that clears the accuracy bar, built so it survives production — with an evaluation harness that proves the choice was right and a cost-per-task budget so the clever version does not quietly become the expensive one. The failure we are called in to fix is almost never the model. It is a retrieval architecture that was chosen by default instead of on purpose.

If you have a retrieval project that works in a demo and wobbles on real data, that gap is usually an architecture mismatch — and it is cheaper to correct on paper than after launch. Book a 30-minute call and we will work out which of these your problem actually needs.