Project 4: Implement a Two-Stage Retrieval System with Reranking

Build a retrieval pipeline with a fast first-stage retriever and a slower, higher-precision reranker.

Quick Reference

Attribute Value
Difficulty Level 3: Advanced
Time Estimate 1–2 weeks
Language Python
Prerequisites Vector search, transformers basics
Key Topics reranking, bi-encoder vs cross-encoder, evaluation

Learning Objectives

By completing this project, you will:

  1. Implement two-stage retrieval (fast candidate + rerank).
  2. Compare bi-encoder vs cross-encoder scoring.
  3. Measure precision/recall vs latency.
  4. Tune top-k and rerank thresholds.
  5. Build an evaluation report.

The Core Question You’re Answering

“How do you trade latency for accuracy in retrieval systems?”

Two-stage retrieval is the standard answer—and this project shows why.

Concepts You Must Understand First

Concept Why It Matters Where to Learn
Bi-encoder retrieval Fast candidate generation Sentence Transformers docs
Cross-encoder rerank High-precision scoring Reranker papers
Precision@k Retrieval evaluation IR metrics
MRR Ranking quality metric Search metrics

Theoretical Foundation

Two-Stage Retrieval Flow

Query -> Retriever (top-50) -> Reranker -> Top-5

The first stage ensures speed; the second stage ensures accuracy.

Project Specification

What You’ll Build

A pipeline that retrieves candidates with a bi-encoder and reranks with a cross-encoder.

Functional Requirements

  1. Retriever with configurable top-k
  2. Cross-encoder reranker
  3. Evaluation metrics (precision@k, MRR)
  4. Latency tracking per stage
  5. Configurable thresholds

Non-Functional Requirements

  • Deterministic evaluation runs
  • Clear logging per stage
  • Fallback mode if reranker fails

Real World Outcome

Example metrics report:

{
  "precision@5": 0.78,
  "mrr": 0.64,
  "latency_ms": {
    "retrieval": 15,
    "rerank": 120
  }
}

Architecture Overview

┌──────────────┐   candidates  ┌──────────────┐
│ Retriever    │──────────────▶│ Reranker     │
└──────────────┘               └──────┬───────┘
                                      ▼
                               ┌──────────────┐
                               │ Evaluator    │
                               └──────────────┘

Implementation Guide

Phase 1: Baseline Retrieval (3–5h)

  • Implement vector retriever
  • Checkpoint: top-50 candidates returned

Phase 2: Reranking (4–8h)

  • Score query-doc pairs
  • Checkpoint: reranked top-5 differs

Phase 3: Evaluation (4–6h)

  • Compute precision@k and MRR
  • Checkpoint: metrics report generated

Common Pitfalls & Debugging

Pitfall Symptom Fix
No improvement same top-5 tune reranker/model
Too slow latency spikes reduce candidate count
Silent failures missing reranker add fallback logs

Interview Questions They’ll Ask

  1. Why is two-stage retrieval better than single-stage?
  2. How do you choose top-k candidate size?
  3. How do you measure reranker impact?

Hints in Layers

  • Hint 1: Start with a fixed retrieval dataset.
  • Hint 2: Add reranker scoring with a small candidate set.
  • Hint 3: Compare metrics before/after reranking.
  • Hint 4: Optimize candidate size for latency.

Learning Milestones

  1. Retrieval Works: candidates returned quickly.
  2. Rerank Works: accuracy improves.
  3. Measured: metrics report produced.

Submission / Completion Criteria

Minimum Completion

  • Two-stage retrieval pipeline

Full Completion

  • Metrics + latency report

Excellence

  • Adaptive top-k or caching

This guide was generated from project_based_ideas/AI_AGENTS_LLM_RAG/GENERATIVE_AI_LLM_RAG_LEARNING_PROJECTS.md.