/learn
What goes into the window
One chapter per mechanism, each opening with an animation. Every chunk, score, ranking and metric is computed by the Agent_Loop_Sim engine's context module from the shipped corpus, embeddings and reranker scores, identically in Python and in your browser. Toggle layers (Concept / Maths / Code) inside any chapter to choose how deep to go. For the slides behind the retrieval chapters, see the RAG and retrieval series.
A twelve-question task in a window too small for it: what the agent knows grows with each read and shrinks when the window is trimmed, compacted or searched again, and what each choice costs in tokens.
BM25 scored live for a query, term by term: inverse document frequency, term-frequency saturation (k1) and length normalisation (b), measured on 200 labelled questions.
Chunks and questions as 384-dimensional embeddings: cosine similarity, nearest neighbours on a 2-D projection, and how much recall survives int8, int4 and one-bit vectors.
Two ranked lists merged by reciprocal rank fusion, rank by rank, then a cross-encoder rereading the top twenty; recall@k for every stage.
- 05Chunking
One text cut three ways, fixed, recursive and semantic: where the boundaries fall, which answers they cut in two, and what each chunking does to recall.
Which retrieved chunks fit a token budget, as a knapsack solved greedily and exactly, and where to put them: an illustrative lost-in-the-middle curve and four placements, measured on 200 questions.
A 36-question task compacted by a perfect and a lossy summariser: which facts survive n compactions, measured over 20 seeded runs, and what searching again costs.
Six sessions and the probes between them: transcript, scratchpad, episodic and semantic memory, with forgetting and consolidation, as facts recalled against tokens spent.
Fifty questions over a document set of up to a million tokens: the whole set in every prompt, with and without the prompt cache, against the top k chunks, in dollars, seconds and answers found.