The five playgrounds from the lesson, on one page. Everything runs in your
browser. Playgrounds 02, 04 and 05 use
real precomputed OpenAI vectors; the chunking is exact browser code.
Playground 01
When do you actually need RAG?
Corpus scale explorerdrag the slider
0
tokens
0
chunks @800
$0
to embed once
0
of a 1M window
Embedding cost assumes text-embedding-3-small at
$0.02 per million tokens. One-off, not per question.
Playground 02 · real vectors
The neighbourhood map
Real embeddings · text-embedding-3-smallclick any word to make it the probe
cat
probe word
—
closest
—
furthest
These are genuine 1,536-dimension OpenAI vectors, precomputed by
tools/export_slide_data.py. The map is a 2-D projection of all 1,536.
Playground 03
Watch the chunks re-cut
Chunking playgroundoverlap is highlighted in each chunk
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chunks
0
avg chars
0%
duplicated
0
est. tokens
Playground 04 · real vectors
Ask, and watch the prompt build
Retrieval playgroundpick a question
0
prompt tokens
0
document tokens
0%
sent to model
0.00
top score
The four preset questions were embedded ahead of time with
text-embedding-3-small, so those scores are real. A question you
type cannot be embedded in a browser — that needs an API call — so it falls back to a rough
local measure, and the panel says which one you are looking at.
Playground 05 · real vectors
Flip the model, break the system
Same store · same question · different query modelreal precomputed scores