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Experiment 03 / Retrieval-augmented generation

Bring
receipts.

A question goes in. An answer comes out. Open the hood and follow everything in between.

Browser-only simulation / No live hardware or AI model
CURRENT EXPERIMENT

Desk Sentinel · online · 22°C · quiet desk

Change readings ↗

Let’s interrogate a tiny machine.

Send a question through a tiny pretend backend. Watch the services pass it along, then inspect what each one did.

This small demo recognizes specific question patterns. Try an example above if it cannot match your wording. Each request captures your readings; questions stay in page memory and are not sent to a server.

A backend you can see through

Follow the question.

SIMULATED SERVICES
REAL DATA FLOW
01 / THINGSSensor readings

Device → telemetry → device twin

Change temperature, light & motion ↗
02 / EYESScene observations

Camera → vision worker → observations

Change the sample scene ↗
03 / FIELD GUIDESource documents

Documents → chunking → search index

Open the six source notes ↓

Upstream paths above show a production design. Here, Things and Eyes save browser state; six authored notes stand in for an indexed knowledge base.

BACKEND BOUNDARY / SIMULATED↓ REQUEST IN · RESPONSE OUT ↑

Ready. Ask a question above, or inspect a service below.

THE ANSWER IS BUILT HERE

Answer service

In a real backend
On this playground
SERVICE PAYLOAD

Run a request to inspect its data.

No request yet. The answer service combines your question, a state snapshot, and retrieved source notes.

One browser worker plays all five services. No HTTP request, cloud database, embedding model, or language model runs here. The service boundaries show where those pieces would live.

Back in the browser

The answer.
With receipts.

The backend builds it. This page displays it. Run a question to see the response arrive.

The RAG bit, without the hand-waving

01 / RETRIEVE

Find the relevant note.

Match your question against this little collection of documents. Similar words find candidate notes; they do not prove a question is answerable.

02 / GROUND

Use actual context.

Combine the retrieved note with the simulation’s current readings.

03 / EXPLAIN

Show the evidence.

Cite the note only for a supported question. A small set of recognized question patterns gates this demo; other phrasing gets a refusal, even when some words match.

In a full RAG system, a language model can generate the response from retrieved context. Here, authored templates make the grounding mechanics visible and deterministic.

GO AGAIN / 01Change the evidence. Ask again.