RAG Showcase
See how Generative AI answers from your own documents.
Large language models are powerful — but they don’t know your contracts, manuals or research, and left to themselves they can confidently invent facts. RAG Showcase is NALITS’ hands-on demonstration of grounding Generative AI in your own content: upload your documents, ask questions, and see the exact passages behind every answer.
Generative AI, Grounded in Your Content
Instead of guessing, the AI answers from your documents — and shows you the evidence.
Upload your own documents
Contracts, policies, manuals, research — whatever your team actually works with. Upload and you're ready.
Ask questions of your content
Ask the AI anything about your documents and get an answer built only from what you uploaded.
See the evidence behind every answer
Every answer shows the exact passages the AI used — so you can verify it before you trust it.
Compare what changes the answers
Try different knowledge-retrieval settings side by side and see how they change the quality of the result.
No retraining required
You don't train or fine-tune a model. Your documents make Generative AI useful to your business immediately.
For Anyone Who Needs Trusted Answers
If you have ever asked these questions, RAG Showcase is where the answers become visible.
- “How do we stop the AI inventing answers?”
- “Can AI answers stay inside our approved content?”
- “Do we need to retrain a model to use our documents?”
- “How do we prove an answer is right before we act on it?”
- Product owners evaluating Generative AI for their business
- Teams that need accurate answers from manuals, policies and research
- Leaders who need to trust what the AI tells them
- Anyone curious to see the engine behind the promise
Two Visual Guides, Real Output
RAG Showcase publishes the results of running its real document engine — no hand-drawn examples. These guides let you see the machinery behind the answers before you experiment.
How documents are prepared for search
Before the AI can search your content, the documents are split into searchable pieces — and the way they are split changes the quality of the answers. This guide explains the strategies and shows the real output of each one on sample texts.
Each strategy controls how much context a piece keeps and where cuts are made — a small choice that can change whether the AI finds the right answer.
Explore the Chunking GuideHow the AI finds the right passages
Six retrieval approaches explored against the same practice texts. A retriever picks the few passages the AI should read for a question — it doesn’t read your whole library.
- Some retrievers match meaning — the same idea worded differently still finds the right passage.
- Some match exact words — great for product codes and names, brittle for everything else.
- The strongest systems combine both, and handle “close but not certain” matches carefully.
Answer Questions. Prove Them.
Upload a document, ask a question and check the evidence for yourself. RAG Showcase is live now at rag.nalits.com.