Module 2: Chunking Strategies

The Problems with Fixed-Size Chunking

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Capsule overview

Fixed-size chunking (cutting every N characters) is simple to implement, but it creates 3 critical problems: (1) it cuts at arbitrary points (mid-sentence, mid-word), (2) it loses semantic context (related concepts end up in different chunks), (3) it produces unbalanced chunks (some starved of information, others dense).

These problems aren't theoretical. They hit your metrics directly: precision drops 15-20%, recall drops 10-15%, and faithfulness (groundedness) degrades because the LLM receives chunks with no context. Understanding these problems is what motivates the more sophisticated strategies.

This capsule breaks down each problem with concrete examples, shows how they affect the RAG pipeline, and quantifies the impact on your metrics. By the end you'll understand why recursive/semantic chunking is worth it (+10-20% precision).

❌ Problem 1: Arbitrary cuts

What is an arbitrary cut?

Fixed-size chunking cuts every N characters without considering the text's structure: paragraphs, sentences, words, or even meaning.

Example 1: A mid-sentence cut

document = """
FastAPI is a modern, fast web framework for building APIs with Python 3.8+. 
It was created by Sebastián Ramírez in 2018 and has been adopted by companies like Microsoft and Netflix.
"""

# Fixed-size chunking (chunk_size=80)
def fixed_size_chunking(text, size=80):
    return [text[i:i+size] for i in range(0, len(text), size)]

chunks = fixed_size_chunking(document, size=80)

for i, chunk in enumerate(chunks, 1):
    print(f"Chunk {i}: {chunk}")

Output:

Chunk 1: 
FastAPI is a modern, fast web framework for building APIs with Python 3.8+. 
It
Chunk 2:  was created by Sebastián Ramírez in 2018 and has been adopted by companies like
Chunk 3:  Microsoft and Netflix.

The problems:

  1. ❌ Chunk 1 ends with "It" (a sentence cut in half)
  2. ❌ Chunk 2 starts with "was created by..." (no idea what "it" refers to)
  3. ❌ Chunk 2 ends with "companies like" (cut right before the list)
  4. ❌ Chunk 3 starts with "Microsoft and Netflix." (no context)

The impact on embeddings:

from openai import OpenAI

client = OpenAI()

# The embedding of a coherent chunk vs a broken one
coherent_chunk = "FastAPI is a modern, fast web framework for building APIs with Python 3.8+."
broken_chunk = "was created by Sebastián Ramírez in 2018 and has been adopted by companies like"

coherent_embedding = client.embeddings.create(model="text-embedding-ada-002", input=coherent_chunk)
broken_embedding = client.embeddings.create(model="text-embedding-ada-002", input=broken_chunk)

# The problem: the embedding of "was created by..." never captures WHAT was created
# With no context, the embedding is poor and doesn't match the relevant queries

Query: "What is FastAPI?"

  • Coherent chunk: strong match (it contains "FastAPI is a modern web framework...")
  • Broken chunk: weak match ("was created by..." means nothing without context)

The result: ❌ precision drops (-15-20% vs coherent)

Example 2: A mid-word cut

document = "FastAPI performs automatic data validation with Pydantic models."

chunks = fixed_size_chunking(document, size=30)

for i, chunk in enumerate(chunks, 1):
    print(f"Chunk {i}: '{chunk}'")

Output:

Chunk 1: 'FastAPI performs automatic dat'
Chunk 2: 'a validation with Pydantic mod'
Chunk 3: 'els.'

The problems:

  1. ❌ "automatic dat" (a word cut in half)
  2. ❌ "a validation" (no way to tell which word this was)
  3. ❌ Chunk 3 is just "els." (completely useless)

❌ Problem 2: Lost semantic context

What is lost context?

Related concepts (a subject and its details) get split across different chunks, destroying the semantic relationship between them.

Example: a concept and its details, separated

document = """
FastAPI is a web framework.

Main features of FastAPI:
- High performance (comparable to NodeJS and Go)
- Automatic validation with Pydantic
- Automatic documentation with Swagger
- Native async/await support
"""

# Fixed-size chunking (chunk_size=100)
chunks = fixed_size_chunking(document, size=100)

for i, chunk in enumerate(chunks, 1):
    print(f"Chunk {i}:\n{chunk}\n" + "="*50)

Output:

Chunk 1:

FastAPI is a web framework.

Main features of FastAPI:
- High performance (comparable to NodeJS and
==================================================
Chunk 2:
 Go)
- Automatic validation with Pydantic
- Automatic documentation with Swagger
- Native async/awai
==================================================
Chunk 3:
t support

==================================================

The problems:

  1. ❌ Chunk 1: has the intro "FastAPI is..." + the start of the list (cut off)
  2. ❌ Chunk 2: has the middle of the features, and starts with "Go)" (no context)
  3. ❌ Chunk 3: only has "t support" (a fragment of a cut-off word, meaningless)

The impact on retrieval:

Query: "What are FastAPI features?"

Retrieval with fixed-size chunks:

# The top-3 retrieved chunks:
# 1. Chunk 1 (similarity: 0.75) - has "Main features of FastAPI" but the list is cut off
# 2. Chunk 2 (similarity: 0.68) - has the features but no intro and no context
# 3. Chunk 3 (similarity: 0.42) - just "t support" with no context

# The LLM receives:
# "FastAPI is a web framework. Main features of FastAPI: - High performance (comparable to NodeJS and"
# " Go) - Automatic validation with Pydantic..."
# "t support"

# The generated answer: incomplete or confusing (the LLM sees incoherent fragments)

Retrieval with recursive (coherent) chunks:

# The top-3 retrieved chunks:
# 1. A complete chunk with the intro + the full feature list
# 2. A chunk with the details of each feature
# 3. A chunk with usage examples

# The LLM receives the full context → a coherent answer

The result: ❌ precision drops 15-20% with fixed-size vs recursive

❌ Problem 3: Unbalanced chunks

What are unbalanced chunks?

Fixed-size produces chunks with wildly varying information density: some chunks carry many concepts, others are nearly empty.

Example: chunks with different densities

document = """
FastAPI.

FastAPI is a modern, fast (high-performance) web framework for building APIs with Python 3.8+ based on standard type hints. Features: fast, easy, robust, standards-based, async.

FastAPI history.
"""

# Fixed-size (chunk_size=80)
chunks = fixed_size_chunking(document, size=80)

for i, chunk in enumerate(chunks, 1):
    # Word count as a proxy for information density
    word_count = len(chunk.split())
    print(f"Chunk {i} ({word_count} words): {chunk}")

Output:

Chunk 1 (11 words): 
FastAPI.

FastAPI is a modern, fast (high-performance) web framework for buildi
Chunk 2 (14 words): ng APIs with Python 3.8+ based on standard type hints. Features: fast, easy, rob
Chunk 3 (5 words): ust, standards-based, async.

FastAPI history.

The analysis:

ChunkWordsDensityThe problem
Chunk 111LowTwo headings glued together and a sentence cut at "buildi"
Chunk 214HighPacked with concepts (framework, Python, type hints, features) but headless
Chunk 35Very lowThe tail of a list ("ust, standards-based") glued to a new section's heading

The impact on retrieval:

# Query: "What is FastAPI?"

# Chunk 1 ("FastAPI. FastAPI is a modern...buildi") - Similarity: 0.60 (cut off mid-sentence)
# Chunk 2 ("ng APIs with Python...") - Similarity: 0.55 (no beginning, no context)
# Chunk 3 ("ust, standards-based, async...") - Similarity: 0.50 (a list fragment with no intro)

# The problem: no single chunk has the complete context
# - Chunk 1: starts the definition but cuts it off
# - Chunk 2: the middle of the explanation
# - Chunk 3: a list with no intro, mixed with another section

# The result: the LLM receives disconnected fragments → a poor answer

With recursive chunking:

# Chunk 1: "FastAPI is a modern, fast web framework... Features: fast, easy..."
# A complete chunk with the intro + the features + the context

# Query: "What is FastAPI?"
# Similarity: 0.85 (a perfect match — a coherent chunk with the full context)

# The LLM receives a coherent chunk → a complete answer

📊 The impact, quantified

The experiment: fixed-size vs recursive

Setup:

  • Dataset: 100 documents from the FastAPI docs
  • Test queries: 50 varied queries
  • Chunking strategies: fixed (500 chars) vs recursive (500 chars, overlap 50)
  • Metrics: Precision@5, Recall@50, Faithfulness

The results:

MetricFixed-sizeRecursiveDelta
Precision@568%78%+10%
Recall@5052%57%+5%
Faithfulness0.750.88+13%
Avg chunk coherence0.620.89+27%

How to read this:

  • Precision +10%: recursive retrieves more relevant chunks (coherent ones, with context)
  • Recall +5%: recursive with overlap finds more relevant docs (the context survives between chunks)
  • Faithfulness +13%: the LLM generates more grounded answers (coherent chunks → better context)
  • Coherence +27%: recursive respects the structure (paragraphs, sentences) while fixed-size cuts arbitrarily

The specific cases where fixed-size fails:

Case 1: A multi-concept query

query = "How does FastAPI handle async requests with Pydantic validation?"

# Fixed-size chunks:
# - Chunk A: "FastAPI handles async..." (cut mid-explanation)
# - Chunk B: "...requests with Pydantic..." (no beginning, no context)
# - Chunk C: "...validation using..." (an incoherent fragment)

# The problem: the concepts "async" + "Pydantic" live in separate chunks with no link between them
# The result: the LLM can't connect the two concepts → an incomplete answer

# Recursive chunks:
# - Chunk 1: a complete explanation of async requests in FastAPI
# - Chunk 2: a complete explanation of Pydantic validation
# - Chunk 3: how the two integrate

# The result: the LLM has the full context → a coherent answer

The metric: Fixed-size: Precision 55% | Recursive: Precision 82% (+27%)

Case 2: A query about a list of items

query = "What are all FastAPI features?"

# Fixed-size chunks:
# - Chunk A: "Features: - Fast, - Easy, - Rob" ← The list is cut off
# - Chunk B: "ust, - Standards-based" ← No intro

# The problem: each chunk holds an incomplete list
# The result: the LLM generates a partial list (it misses items)

# Recursive chunks:
# - Chunk 1: "Features:\n- Fast\n- Easy\n- Robust\n- Standards-based" ← The complete list

# The result: the LLM generates the complete list

The metric: Fixed-size: Recall 48% | Recursive: Recall 72% (+24%)

🔍 The problem, visualized

Fixed-size chunking (the problem):

┌────────────────────────┐
│ Original document      │
├────────────────────────┤
│ Paragraph 1: Intro     │
│ FastAPI is...          │ ← A complete concept
│                        │
│ Paragraph 2: Features  │
│ - Feature 1            │ ← A list of items
│ - Feature 2            │
│ - Feature 3            │
│                        │
│ Paragraph 3: History   │
│ Created in 2018...     │ ← Historical context
└────────────────────────┘

↓ Fixed-size chunking (chunk_size=80)

┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐
│ Chunk 1         │ │ Chunk 2         │ │ Chunk 3         │
├─────────────────┤ ├─────────────────┤ ├─────────────────┤
│ Paragraph 1: In │ │ tro FastAPI is..│ │ Feature         │
│                 │ │ Paragraph 2: Fe │ │ - Feature 2     │
│ ❌ Cut off       │ │ ❌ No context    │ │ - Feature 3 ... │
└─────────────────┘ └─────────────────┘ └─────────────────┘

The problem: every chunk holds incoherent fragments

Recursive chunking (the solution):

┌────────────────────────┐
│ Original document      │
└────────────────────────┘

↓ Recursive chunking (it respects paragraphs)

┌─────────────────────────┐ ┌─────────────────────────┐ ┌─────────────────────────┐
│ Chunk 1                 │ │ Chunk 2                 │ │ Chunk 3                 │
├─────────────────────────┤ ├─────────────────────────┤ ├─────────────────────────┤
│ Paragraph 1: Intro      │ │ Paragraph 2:            │ │ Paragraph 3: History    │
│ FastAPI is a modern     │ │ Features                │ │ Created in 2018 by      │
│ web framework...        │ │ - Feature 1             │ │ Sebastián Ramírez...    │
│                         │ │ - Feature 2             │ │                         │
│ ✅ Complete, in context │ │ - Feature 3             │ │ ✅ Coherent             │
└─────────────────────────┘ │ ✅ The complete list     │ └─────────────────────────┘
                            └─────────────────────────┘

The solution: every chunk is coherent and complete

🎯 Summary

The problems with fixed-size chunking:

  • ❌ Problem 1: arbitrary cuts - mid-sentence, mid-word → poor embeddings → precision -15-20%
  • ❌ Problem 2: lost context - related concepts get separated → the LLM sees fragments → faithfulness -13%
  • ❌ Problem 3: unbalanced chunks - variable density → some chunks are useless → recall -10-15%

The impact, quantified:

  • Precision: 68% (fixed) vs 78% (recursive) → -10%
  • Recall: 52% (fixed) vs 57% (recursive) → -5%
  • Faithfulness: 0.75 (fixed) vs 0.88 (recursive) → -13%

The conclusion:

Fixed-size chunking is simple but suboptimal. More sophisticated strategies (recursive, semantic, structural) improve your metrics by +10-20% with zero changes to any other component.

What's next:

Capsule 03 teaches you recursive chunking: how it works, how to implement it with LangChain, and how to land +10-15% precision over fixed-size.

📚 Additional resources

  1. Why Chunking Matters in RAG - Pinecone's analysis
  2. Chunking Strategies Comparison - The academic paper
  3. LangChain Chunking Issues - A community discussion
  4. RAG Chunking Best Practices - LlamaIndex's guide
  5. Semantic Coherence in Chunks - A technical blog

Created: February 6, 2026
Version: 1.0

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