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Python / Python Modern Generative AI and Agents Interview Questions

What are the most important text splitting strategies in RAG, and how do chunk size and overlap affect retrieval quality?

Chunk size and overlap are the most impactful hyperparameters in a RAG pipeline — they directly affect both retrieval precision and answer quality. A chunk that is too small may contain only a fragment of a complete thought; a chunk that is too large may contain so much irrelevant content that the LLM's attention is diluted and cost increases.

Text Splitting Strategies
SplitterLogicBest for
CharacterTextSplitterSplit on a single separator character (e.g. newline)Simple documents with clear delimiters
RecursiveCharacterTextSplitterTry paragraph → sentence → word splits in order until chunks are small enoughGeneral purpose; most common default
TokenTextSplitterSplit by actual model tokens, not charactersPrecise context window management
MarkdownHeaderTextSplitterSplit at Markdown headers, preserving structure in metadataTechnical docs, wikis, README files
SemanticChunkerEmbed sentences, split where embedding similarity dropsDense prose without clear structure
from langchain_text_splitters import (
    RecursiveCharacterTextSplitter,
    MarkdownHeaderTextSplitter,
    TokenTextSplitter,
)

# ── RecursiveCharacterTextSplitter — general default
splitter = RecursiveCharacterTextSplitter(
    chunk_size=1000,      # characters per chunk
    chunk_overlap=200,    # overlap to avoid cutting mid-thought
    separators=['\n\n', '\n', '.', ' ', ''],  # try in order
    length_function=len,  # can swap for token-counting function
)

# ── TokenTextSplitter — respect model context window precisely
from langchain_openai import OpenAIEmbeddings
token_splitter = TokenTextSplitter(
    encoding_name='cl100k_base',  # GPT-4 / text-embedding-3 encoding
    chunk_size=256,               # tokens per chunk
    chunk_overlap=50,
)

# ── MarkdownHeaderTextSplitter — preserves document structure
md_splitter = MarkdownHeaderTextSplitter(
    headers_to_split_on=[
        ('#',  'section'),
        ('##', 'subsection'),
    ]
)
md_text = '# Introduction\nWelcome!\n## Background\nSome history...'
sections = md_splitter.split_text(md_text)
for s in sections:
    print(s.page_content, s.metadata)

# Rule of thumb for chunk_size:
# - 256–512 tokens: high precision retrieval, lower recall
# - 512–1024 tokens: balanced; most common for dense docs
# - 1024–2048 tokens: higher recall, more noise per chunk

Why is chunk_overlap important in text splitting for RAG?
When should you prefer TokenTextSplitter over RecursiveCharacterTextSplitter?

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