Tensor TownISSUE #120 of 120

references · papers with code · arxiv · sota

NeuraVSThe Overfit Ogre

Neura saysYou’ve covered AI end to end — now build something real with an API, RAG, or a small model.

AI clicks when you build. The capstone: ship a small AI feature — a RAG "chat with your docs" (embed, store, retrieve, answer with citations), a function-calling agent that uses one tool, or a fine-tuned/quantized local model for a narrow task. Add an eval set and measure it. That end-to-end loop — data, model, prompt, eval — is the real skill. References: papers-with-code, arXiv, model/provider docs, and the eval harnesses.

Power-ups you unlock

Build to retain — ship one real AI feature
RAG, a tool-using agent, or a local model
Add an eval set and measure
Refs: papers-with-code, arXiv, provider docs

The Overfit Ogre attacks — common mistakes

Reading without building
No eval set → can’t tell if it works
Reaching for fine-tuning before prompt/RAG

Boss battlePlan a RAG app: ingest docs, retrieve, answer with citations, and an eval set.

Example code

<!doctype html><html><head><meta charset="utf-8"></head>
<body style="background:#06040d;color:#e6e0ff;font-family:monospace;padding:20px"><pre>1 chunk + embed docs → vector DB
2 query → retrieve → answer with citations
3 eval set of real questions → measure
loop: data · model · prompt · eval</pre></body></html>

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