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Designing an Adaptive Chunking Engine for Real-World RAG Systems

  • 18 hours ago
  • 4 min read




Objective

In this assignment, you will move beyond isolated chunking techniques and design a complete, adaptive chunking system that intelligently selects or combines strategies based on the input document type.


This is closer to how chunking is actually used in production systems.





Problem Statement

Most tutorials treat chunking strategies independently:


  • Fixed-size chunking

  • Overlapping chunking

  • Sentence-based chunking

  • Token-aware chunking

  • Semantic chunking


However, in real-world systems:


No single strategy works for all document types.


Your task is to build a chunking engine that:


  1. Detects document structure/type

  2. Selects the appropriate chunking strategy

  3. Applies it effectively

  4. Produces high-quality chunks for retrieval





Task Breakdown




 Task 1 — Implement Core Chunking Strategies


Implement the following functions:


  • fixed_size_chunk(text, chunk_size)

  • chunk_with_overlap(text, size, overlap)

  • sentence_chunker(sentences, max_words)

  • token_chunk(text, chunk_size, overlap)

  • semantic_chunk(sentences, embeddings, threshold)



 Requirement:


  • Each function must be modular and reusable

  • Add docstrings explaining behavior and assumptions




 Task 2 — Document Type Detection


Create a function:


def detect_document_type(text):

   ...

It should classify input into categories such as:


  • Plain text

  • Structured markdown

  • Technical documentation

  • Narrative/paragraph text




 Hint: Use heuristics such as:


  • Presence of headers (#, <h1>)

  • Sentence density

  • Paragraph spacing

  • Average sentence length




 Task 3 — Strategy Selection Engine


Create a controller:



def chunk_document(text):

   ...


This function should:


  • Detect document type

  • Choose appropriate strategy:



Document Type

Suggested Strategy

Markdown

Structure-aware chunking

Technical docs

Sentence + token-aware

Narrative text

Semantic chunking

Raw text

Fixed / overlap chunking


 You are free to design your own logic.




 Task 4 — Hybrid Chunking


Extend your system to support hybrid strategies, such as:


  • Structure → Sentence → Token normalization

  • Sentence → Semantic refinement

  • Fixed → Overlap → Token limit enforcement


 Output should be:



[
 {
   "chunk": "...",
   "strategy": "semantic + token",
   "length": 78,
   "tokens": 120
 }
]



 Task 5 — Evaluation Framework


Design a simple evaluation system:



def evaluate_chunks(chunks):

   ...

Evaluate based on:


  • Chunk size consistency

  • Context preservation

  • Redundancy (overlap quality)

  • Semantic coherence



 You may:


  • Use cosine similarity between sentences

  • Track variance in chunk lengths

  • Analyze token distribution




 Task 6 — Comparative Experiment


Run your system on at least 3 different types of documents:


  1. Markdown file

  2. Technical explanation (e.g., Transformers)

  3. Mixed paragraph text



Compare:


  • Number of chunks

  • Average size

  • Retrieval readiness (qualitative)





 Deliverables


Submit the following:


1.  Code Repository





2.  Report (1500–2000 words)

Your report must include:




 System Design


  • Strategy selection logic

  • Why certain strategies were chosen




 Trade-offs


  • Where fixed chunking fails

  • When semantic chunking helps/hurts




 Hybrid Strategy Justification

Why layering improves results




 Observations


  • Differences across document types

  • Any surprising results



3.  Output Samples


Include:

  • Sample chunks from each document

  • Annotated explanation of chunk quality





 Bonus (Optional)

  • Integrate with a vector DB (e.g., Chroma)

  • Run a retrieval query and show results

  • Build a small UI to visualize chunks





 Evaluation Rubric


Criteria

Weight

Strategy Implementation

20%

Document Detection Logic

15%

Adaptive System Design

20%

Hybrid Strategy Effectiveness

15%

Evaluation Framework

10%

Report Quality

10%

Code Quality & Modularity

10%





 Guidelines


  • Avoid hardcoding logic for specific texts

  • Write reusable and extensible code

  • Focus on reasoning, not just implementation

  • Clearly document assumptions





 Submission Instructions


  • Submit via LMS (Moodle / portal)

  • Upload:

    • Code (ZIP or GitHub link)

    • Report (PDF)

    • Output samples


 Deadline: [Instructor to specify]





Final Note

This assignment is intentionally open-ended.

In real-world AI systems, chunking is not a function — it’s a design decision.

Your goal is to think like a system designer, not just a coder.





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