Project 5: Fine-Tune Your Own Embedding Model

Fine-tune a sentence embedding model on domain data and measure retrieval quality improvements.

Quick Reference

Attribute Value
Difficulty Level 4: Expert
Time Estimate 2–3 weeks
Language Python
Prerequisites PyTorch basics, embeddings, evaluation
Key Topics contrastive loss, triplet mining, retrieval evals

Learning Objectives

By completing this project, you will:

  1. Build a domain dataset of positives/negatives.
  2. Train embeddings with contrastive or triplet loss.
  3. Evaluate retrieval metrics before and after tuning.
  4. Detect embedding drift and overfitting.
  5. Export the tuned model for production use.

The Core Question You’re Answering

“How do you move from generic embeddings to domain-specific accuracy?”

Fine-tuning is the difference between okay and production-grade retrieval.

Concepts You Must Understand First

Concept Why It Matters Where to Learn
Contrastive learning Core embedding training Metric learning papers
Hard negatives Improve retrieval quality IR training guides
Recall@k / MRR Measure retrieval improvements Evaluation metrics
Overfitting Avoid embedding drift Training basics

Theoretical Foundation

Fine-Tuning Loop

Pairs/Triplets -> Training -> Embeddings -> Retrieval Eval

The key is measuring retrieval metrics before and after tuning.

Project Specification

What You’ll Build

A training pipeline that fine-tunes an embedding model and compares retrieval metrics to baseline.

Functional Requirements

  1. Dataset builder for pairs/triplets
  2. Training loop with contrastive loss
  3. Evaluation suite (recall@k, MRR)
  4. Baseline comparison report
  5. Model export with config

Non-Functional Requirements

  • Deterministic splits and seeds
  • No leakage between train/test
  • Clear metrics reporting

Real World Outcome

Example report:

{
  "baseline_recall@10": 0.62,
  "tuned_recall@10": 0.78,
  "mrr": 0.51,
  "delta": "+0.16"
}

Architecture Overview

┌──────────────┐   pairs   ┌──────────────┐
│ Dataset      │────────▶│ Trainer       │
└──────────────┘         └──────┬───────┘
                                ▼
                         ┌──────────────┐
                         │ Evaluator    │
                         └──────────────┘

Implementation Guide

Phase 1: Dataset + Baseline (4–6h)

  • Build pair/triplet dataset
  • Run baseline evaluation
  • Checkpoint: baseline metrics recorded

Phase 2: Fine-Tuning (8–12h)

  • Train with contrastive or triplet loss
  • Checkpoint: training loss decreases

Phase 3: Post-Eval + Export (4–8h)

  • Compare tuned vs baseline
  • Export model and configs
  • Checkpoint: metrics report generated

Common Pitfalls & Debugging

Pitfall Symptom Fix
Data leakage inflated metrics strict split by ID
Overfitting eval drops add hard negatives
Drift worse generalization mix domain + general samples

Interview Questions They’ll Ask

  1. Why do hard negatives improve retrieval performance?
  2. How do you detect embedding drift?
  3. What metrics best measure retrieval quality?

Hints in Layers

  • Hint 1: Start with a small labeled dataset.
  • Hint 2: Add hard negatives to improve contrast.
  • Hint 3: Compare recall@k before/after tuning.
  • Hint 4: Export and test the model in a retrieval demo.

Learning Milestones

  1. Baseline Measured: initial retrieval metrics established.
  2. Model Tuned: improved metrics achieved.
  3. Production Ready: tuned model exported.

Submission / Completion Criteria

Minimum Completion

  • Dataset + fine-tuning loop

Full Completion

  • Evaluation report + model export

Excellence

  • Hard negative mining
  • Multi-task evaluation

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