start with the idea before the implementation.
the mechanisms you need to reason about.
Idf
Idf 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.
Term saturation
Term saturation 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.
Length normalization
Length normalization 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.
Top-k retrieval
Top-k retrieval 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.
turn the lesson into evidence.
implement BM25 scoring
Build the smallest version first. Record the input, expected output, measured result and one failure you discovered.
tune k1 and b
Build the smallest version first. Record the input, expected output, measured result and one failure you discovered.
evaluate MRR
Build the smallest version first. Record the input, expected output, measured result and one failure you discovered.
prove you can explain and decide.
explain why repetition saturates
ask cortex to test me →measure nDCG
ask cortex to test me →compare to TF-IDF
ask cortex to test me →what usually goes wrong.
no semantic equivalence
Detect this early by defining a baseline, a measurable signal and a condition that would cause you to stop or redesign the approach.
bad tokenization
Detect this early by defining a baseline, a measurable signal and a condition that would cause you to stop or redesign the approach.
evaluation without judged relevance
Detect this early by defining a baseline, a measurable signal and a condition that would cause you to stop or redesign the approach.
Create a short BM25 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.