PlainLogic

Interactive lab · Practical AI

RAG: Answers Grounded in Your Documents

RAG hands the model cheat sheets: it searches your documents first, then writes the answer with the evidence in front of it.

Run the flow

The experiment

Press Run, watch the idea move

This animated diagram is powered by the PlainLogic flow engine. Follow the packet along the edges — every step is labeled as it fires.

Everything here is a simplified educational visualization — the shape of the idea, not real model internals. The Run button drives a simulated animation in your browser; no real AI model runs and nothing is sent anywhere.

The RAG flow — answers with receipts

Simplified educational visualization

RAG (retrieval-augmented generation) fixes the model's knowledge cutoff by handing it cheat sheets. Your question is turned into a search, matching documents are pulled from a knowledge store, and the best chunks are pasted into the model's context before it writes the answer. This is simplified: real systems use vector indexes and similarity search to find those chunks — the diagram shows the shape of the idea, not the machinery.

Ready. Press Run to watch a question get answered with documents.

Simplified educational visualization. No real embeddings, search, or retrieval runs here; the packets are illustrative.

Plain-language AI

In plain logic

RAG has two jobs: first retrieve useful passages, then generate an answer with those passages in context. The model itself never learns your documents — it just reads the retrieved chunks while writing, the same way you would answer a quiz with an open book.

How does retrieval find the right passages? Three common ways: keyword search (match the words), embedding search (match the meaning — see the embeddings guide), or hybrid (both, then merged). Big document stores use vector indexes so the search stays fast across millions of chunks.

The result is an answer that can point at its sources — answers with receipts. But note the honest framing: RAG does not automatically train the model on your documents. Remove the documents and the knowledge is gone again.

Hands-on

Try this

Imagine a tiny shop handbook with one line: “Returns are accepted within 30 days with a receipt.” Ask “Can I return this?” and a RAG system finds that passage, quotes it, and answers from it.

Now remove the handbook and ask again. A well-built system should say it lacks evidence — “I don't have the returns policy.” That admission is a feature, not a bug: it is the difference between an answer grounded in your documents and a confident guess wearing an answer's clothes.

Honest boundaries

What this leaves out

RAG improves grounding but guarantees nothing. It can retrieve the wrong passage, work from stale information, or — the subtle one — retrieve a perfect passage and then misrepresent it in the generated answer. The model is still predicting likely text; it can bend your evidence while quoting it.

Also remember: retrieval respects no permissions on its own. If the knowledge store contains documents the asker should not see, the system needs access controls — otherwise RAG becomes a very efficient way to leak private text into answers. Always check sources, dates, and access permissions.

Honest answers

Questions people ask

Is RAG the same as fine-tuning?

No. Fine-tuning rewrites the model's weights with new training; RAG leaves the model untouched and just hands it documents to read. RAG is cheaper, faster to update, and lets the answer cite sources — fine-tuning is for teaching behavior, not facts.

Does RAG stop hallucinations?

It reduces them a lot, but it does not stop them. A model can still invent details, quote a passage out of context, or answer from the wrong retrieved chunk. RAG narrows the gap between evidence and answer; it never closes it.

What makes RAG fail in practice?

Bad chunking (passages cut mid-thought), stale documents, retrieval that returns near-misses instead of the right passage, and context stuffed with so many chunks that the model loses the signal. Most real RAG work is fixing retrieval, not the model.