Phase 2: Sequence Models · ~75 minutes · Python

Information Retrieval and Search

BM25 is precise but brittle. Dense casts a wide net but misses keywords. Hybrid is the 2026 default. Everything else is tuning.

What you will learn

Implement information retrieval and search from scratch
Understand the math and intuition behind the algorithm
Use production libraries for the same task
Ship a reusable artifact

Introduction

Type: Build
Languages: Python
Prerequisites: Bag of Words, TF-IDF, and Text Representation, GloVe, FastText, and Subword Embeddings
Time: ~75 minutes

The Problem

The user types "what happens if someone lies to get money" and expects to find the statute that actually covers that: "Section 420 IPC." A keyword search misses it entirely (no shared vocabulary). A semantic search misses it if the embeddings were not trained on legal text. Real search has to handle both.

IR is the pipeline under every RAG system, every search bar, every docs site's fuzzy lookup. The 2026 architecture that works in production is not a single method. It is a chain of complementary methods, each catching the failures of the one before.

This lesson builds each piece and names which failures each catches.

Unlock the full lesson

You've read the first 2 sections. The rest of this lesson covers The Concept, Build, Check Yourself, Key Terms & Next — plus a hands-on lab, quiz, and project artifact.

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Key terms in this lesson

RAG (Retrieval-Augmented Generation)