TF-IDF (Term Frequency - Inverse Document Frequency)
Statistical Term Importance Weighting for Sparse Document Vectors & Keyword Extraction
#NLP#Information Retrieval#TF-IDF#Sparse Vectors#Keywords
Choose Presentation Mode:
STAGE 1 / 7— Anti-Pattern
Section 1: The LLM Anti-Pattern vs Right-Sized Model
The Naive Generative LLM Approach:
Calling an LLM API to extract top 5 representative topic keywords from 100,000 blog articles.
Why It Fails in Production:
Costs hundreds of dollars in API tokens and takes hours; TF-IDF extracts unique discriminative keywords across the entire corpus in seconds.
Targeted Algorithm (TF-IDF (Term Frequency - Inverse Document Frequency))
Latency:0.08ms per doc
Cost / 1M Ops:$0.00
Determinism:Exact Statistical Importance
Generative LLM Alternative
Latency:1,500ms
Cost / 1M Ops:$4,000
Determinism:Subjective Word Suggestions