Designing a Production-Ready Chunking Pipeline for RAG

Course: Chunking Strategies for Production RAG Systems

Level: Medium → Advanced

Type: Individual Assignment

Duration: 5–7 days

Objective

The objective of this assignment is to help you:

• Understand and implement multiple chunking strategies

• Analyze trade-offs between different approaches

• Design a hybrid chunking pipeline

• Evaluate chunking quality in a Retrieval-Augmented Generation (RAG) context

• Think like an engineer building production-ready systems

Problem Statement

You are given a set of mixed-format documents (plain text, markdown, and HTML).

Your task is to: Design, implement, and evaluate a chunking pipeline that produces high-quality chunks optimized for retrieval.

Dataset Description

You must work with at least 3 types of input data:

Required formats:

• Plain text document

• Markdown document (with headings and sections)

• HTML document (with headings and structured content)

Suggested sources:

• Technical blogs

• Documentation pages

• Research articles

Each document should be at least 500–1500 words

Tasks & Requirements

Task 1: Baseline Chunking (10 Marks)

Implement at least 2 basic chunking strategies:

• Fixed-size chunking (word-based or character-based)

• Sentence-based chunking

Requirements:

• Clearly define chunk size

• Print sample outputs

• Explain limitations

Task 2: Token-Aware Chunking (15 Marks)

Implement token-based chunking using a tokenizer (e.g., tiktoken).

Requirements:

• Define chunk_size and overlap

• Show token length for each chunk

• Ensure chunks stay within limits

Analysis:

• Compare with fixed-size chunking

• Discuss token efficiency

Task 3: Sliding Window Chunking (10 Marks)

Implement chunking with overlap.

Requirements:

• Define window_size and stride

• Show how overlap preserves context

Analysis:

• When is overlap useful?

• Trade-offs (redundancy vs context preservation)

Task 4: Semantic Chunking (20 Marks)

Use embeddings to group semantically similar sentences.

Requirements:

• Use a model like:

  • sentence-transformers

• Compute similarity between sentences

• Split chunks based on a similarity threshold

Analysis:

• Experiment with at least 3 threshold values

• Compare chunk boundaries

• Explain how meaning is preserved

Task 5: Structure-Aware Chunking (15 Marks)

Handle structured documents.

Markdown:

• Split based on headers

HTML:

• Extract sections using heading tags (h1, h2, etc.)

Requirements:

• Preserve section-level grouping

• Avoid mixing unrelated sections

Task 6: Hybrid Chunking Pipeline (20 Marks)

Design a combined pipeline using:

• Structure-aware splitting

• Semantic grouping

• Token normalization

• Optional overlap

Requirements:

• Clearly define pipeline steps

• Show final chunk outputs

• Ensure chunks are:

  • coherent

  • within token limits

  • context-preserving

Task 7: Evaluation & Insights (10 Marks)

Evaluate your chunking strategies.

You must answer:

• Which method produced the best chunks and why?

• What trade-offs did you observe?

• How does chunking affect retrieval quality?

Optional but recommended: Run a simple retrieval example using embeddings

Deliverables

You must submit:

1. Code (Required)

• Jupyter Notebook (.ipynb)

• Well-commented code

• Clear sectioning for each task

2. Report (Required)

A short report (3–5 pages) including:

• Approach for each task

• Observations

• Comparisons

• Final pipeline design

• Key learnings

  
  

Format: PDF or DOCX

3. Output Samples (Required)

Include:

• Sample chunks from each strategy

• Final hybrid chunks

Submission Guidelines

Submit via your LMS (e.g., Moodle / Google Classroom).

File Naming Convention: <YourName>_Chunking_Assignment.zip

Inside the ZIP:

• /notebook.ipynb 

• /report.pdf 

• /data/ (optional) 

Deadline: Submit within 7 days from assignment release

Late Submission Policy:

• Up to 24 hours late → 10% penalty

• 24–48 hours → 20% penalty

• Beyond 48 hours → Not accepted

Important Instructions

• Do NOT copy code from external sources without understanding

• You must explain your logic clearly

• Use of libraries is allowed, but core logic must be implemented by you

• Plagiarism will result in disqualification

Evaluation Rubric

Criteria	Marks
Basic Chunking	10
Token-Aware Chunking	15
Sliding Window	10
Semantic Chunking	20
Structure-Aware Chunking	15
Hybrid Pipeline	20
Analysis & Insights	10
Total	100

Guidance & Tips

• Start simple → then build complexity

• Visualize chunks wherever possible

• Focus on why a chunk is good or bad

• Don’t just implement — analyze deeply

• Think from a retrieval perspective, not just splitting

Bonus (Optional — up to +10 Marks)

• Build a mini RAG demo using your chunks

• Compare retrieval quality across strategies

• Visualize similarity scores

Instructor Note

This assignment is designed to simulate real-world system design thinking.

There is no single correct answer.

What matters is:

• clarity of reasoning

• quality of implementation

• depth of analysis

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