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Academy/NLP & Retrieval Engineering/TF-IDF
module 2 of 7 · 40 min

TF-IDF

Create sparse text vectors that emphasize terms important in one document but rare globally.

Explain TF-IDF clearlyImplement a small TF-IDF exampleEvaluate whether TF-IDF improves a simpler baselineIdentify failure cases and operational constraints
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mental model

start with the idea before the implementation.

Local frequency says a term matters here while inverse document frequency says whether it is distinctive.
core concepts

the mechanisms you need to reason about.

01

Term frequency

Term frequency is studied through intuition implementation evidence and trade-offs. The goal is to be able to explain the mechanism and verify it with a concrete test rather than only repeat a definition.

02

Document frequency

Document frequency is studied through intuition implementation evidence and trade-offs. The goal is to be able to explain the mechanism and verify it with a concrete test rather than only repeat a definition.

03

Idf smoothing

Idf smoothing is studied through intuition implementation evidence and trade-offs. The goal is to be able to explain the mechanism and verify it with a concrete test rather than only repeat a definition.

04

Cosine similarity

Cosine similarity is studied through intuition implementation evidence and trade-offs. The goal is to be able to explain the mechanism and verify it with a concrete test rather than only repeat a definition.

engineering lab

turn the lesson into evidence.

LAB 1

vectorize a mini corpus

Build the smallest version first. Record the input, expected output, measured result and one failure you discovered.

LAB 2

rank by cosine similarity

Build the smallest version first. Record the input, expected output, measured result and one failure you discovered.

LAB 3

inspect top weighted terms

Build the smallest version first. Record the input, expected output, measured result and one failure you discovered.

knowledge checks

prove you can explain and decide.

1

explain sparse vectors

ask cortex to test me →
2

identify vocabulary drift

ask cortex to test me →
3

compare with BM25

ask cortex to test me →
failure modes

what usually goes wrong.

risk

weak semantics

Detect this early by defining a baseline, a measurable signal and a condition that would cause you to stop or redesign the approach.

risk

huge vocabularies

Detect this early by defining a baseline, a measurable signal and a condition that would cause you to stop or redesign the approach.

risk

inconsistent preprocessing

Detect this early by defining a baseline, a measurable signal and a condition that would cause you to stop or redesign the approach.

proof of learning

Create a short TF-IDF engineering note with one working artifact one metric one failure case and one decision about when you would or would not use it.

Save the result in your portfolio or project repository. A strong learning artifact should make your assumptions, metrics and failure analysis visible.