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Evaluation of Fixed-Length and Semantic Chunking in RAG

BeginnerGuided Project

Learn to build and evaluate a Retrieval-Augmented Generation system using different document chunking strategies. This project introduces a controlled experiment comparing fixed-length and semantic chunking under identical retrieval and generation settings. You will implement dense embeddings, construct FAISS vector indexes, perform top-K similarity search, and generate grounded responses with an LLM. You will gain practical insight into how preprocessing decisions influence retrieval coherence and downstream language model behavior, strengthening your foundation in modern RAG system design.

4.8 (18 Reviews)

Language

  • English

Topic

  • Artificial Intelligence

Enrollment Count

  • 132

Skills You Will Learn

  • Faiss, Retrieval-Augmented Generation (RAG), Sentence Embeddings, Similarity Search, Text Segmentation, Vector Indexing

Offered By

  • IBMSkillsNetwork

Estimated Effort

  • 60 minutes

Platform

  • SkillsNetwork

Last Update

  • March 17, 2026
About this Guided Project
In this project, you will explore how document chunking strategies directly influence retrieval quality and downstream language model behavior in Retrieval-Augmented Generation (RAG) systems. You will build a controlled RAG workflow to compare fixed-length and semantic chunking under identical embedding, indexing, retrieval, and generation settings.

Who Is It For

This project is designed for learners with foundational Python and machine learning knowledge, such as software engineers, data scientists, or AI practitioners, who want to understand how preprocessing decisions impact retrieval-augmented generation (RAG) systems. It is especially relevant for those familiar with embeddings and vector search who want deeper insight into how document segmentation affects retrieval quality, evidence grounding, and downstream language model behavior in production RAG pipelines.

What You’ll Learn

By the end of this project, you will understand how chunking strategy shapes vector index structure, retrieval alignment, and downstream generation quality in Retrieval-Augmented Generation systems. You will see how fixed-length chunking differs from semantic chunking in both structural properties and retrieval outcomes, and why preprocessing decisions materially affect answer grounding. You will be able to:
  • Learn how to implement fixed-length and semantic chunking and analyze their structural differences.
  • Build a compact RAG pipeline that generates dense embeddings, constructs FAISS similarity indexes, retrieves top-K evidence, and produces grounded responses with an LLM.

What You'll Need

You should be comfortable writing basic Python code and working with common data structures. No prior experience with advanced RAG evaluation is required. All required libraries can be installed directly within the IBM Skills Network Labs environment, allowing you to run the full workflow without external configuration. The project works best on modern browsers such as Chrome, Edge, Firefox, or Safari.