I Stopped Using Libraries and Built TF-IDF From Scratch to Truly Understand Retrieval

2026-03-02 · Source: NLP on Medium · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Advanced, short

Summary

An engineer developed a complete TF-IDF based retrieval and evaluation system from scratch in Jupyter to deeply understand the mathematical foundations of search, preceding work with RAG systems and embeddings. The process involved splitting a philosophical passage into sentences, designing a custom preprocessing pipeline with specific stopwords, and explicitly constructing a vocabulary to define the vector space. A TF-IDF class was implemented, including a smoothed IDF formula `log((N + 1) / (df + 1)) + 1` and L2 normalization for term frequency vectors. Manual cosine similarity was defined, and a retrieval function was built to rank sentences. An auto-generated evaluation dataset was created, and the system's accuracy was measured using metrics like Mean Reciprocal Rank, Precision@3, Recall@3, and NDCG@3. The custom implementation was benchmarked against `TfidfVectorizer` from scikit-learn, validating its correctness. The same TF-IDF vectors were then extended to power an extractive summarization system.

Key takeaway

For AI Engineers and Data Scientists building or debugging retrieval-augmented generation (RAG) systems, understanding the foundational vector math of TF-IDF is crucial. Your ability to diagnose issues like embedding complexity or ranking instability will improve by grasping how text transforms into vectors, how similarity is measured, and how results are ranked. Consider implementing a core retrieval system from first principles to solidify this intuition, even if you ultimately use advanced libraries.

Key insights

Building TF-IDF from scratch clarifies the vector math underlying modern retrieval and RAG systems.

Principles

• Retrieval systems search structured units, not raw documents.
• Text representation as vectors enables linear algebra for search.
• Understanding without verification is assumption.

Method

Implement TF-IDF by defining custom preprocessing, vocabulary, TF-IDF class with smoothed IDF, manual cosine similarity, and a retrieval function. Evaluate with auto-generated queries and ranking metrics.

In practice

• Split documents into structured units like sentences.
• Define custom stopwords and preprocessing for control.
• Benchmark custom implementations against library versions.

Topics

• TF-IDF
• Information Retrieval
• Vector Space Models
• Evaluation Frameworks
• Extractive Summarization

Code references

• sarveshtalele/neural-network-from-scratch-with-maths

Best for: Machine Learning Engineer, AI Engineer, Data Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by NLP on Medium.