How Should You Chunk Documents for AI Search?

Chunking is one of those quiet decisions that can make AI search feel crisp and helpful—or scattered and frustrating. Good chunking turns long documents into search-friendly pieces that match user questions, preserve meaning, and keep retrieval costs under control. Below are practical strategies you can apply to get better results from keyword search, vector search, or hybrid retrieval.

Why chunking matters for AI search

Most AI search systems retrieve a limited number of text segments (chunks) and then either display them or pass them to a language model for an answer. If chunks are too big, retrieval gets noisy and expensive, and the model may miss the relevant part. If chunks are too small, meaning breaks apart, context disappears, and the system returns fragments that don’t answer the question.

Great chunking balances four goals:

• Relevance: the chunk is likely to match a query.
• Coherence: the chunk makes sense on its own.
• Coverage: the set of chunks represents the full document well.
• Efficiency: minimal wasted tokens and indexing overhead.

Start with the user’s query patterns

Chunking works best when it matches how people ask questions. Before picking a chunk size, look at real queries (or expected ones) and classify them:

• Fact lookups: “What is the refund window?”
• Procedures: “How do I reset a device?”
• Troubleshooting: “Why is syncing failing?”
• Definitions and policy: “What counts as personal data?”
• Comparisons: “Standard vs premium plan differences?”

Procedures and troubleshooting often require multiple steps and context, so they benefit from slightly larger chunks or multi-chunk retrieval. Fact lookups can work well with smaller chunks—if each chunk still contains the full fact and its conditions.

Prefer semantic boundaries over fixed length

A common mistake is splitting text every N characters or tokens with no regard for meaning. Fixed-length chunking is easy, but it cuts through headings, lists, tables, and arguments.

Better: chunk along semantic boundaries, such as:

• Headings and subheadings
• Paragraph breaks
• List blocks (keep a whole list together when possible)
• Table sections (or table rows grouped by topic)
• Q&A pairs (question + answer in one chunk)

If your documents are structured (Markdown, HTML, DOCX with styles), use that structure. If they’re plain text, you can still infer boundaries using blank lines, numbering patterns, and punctuation.

Use a “target size + flexibility” approach

Instead of a strict size, use a target range, then flex to preserve meaning.

A practical baseline for many systems:

• Target: 250–500 tokens per chunk
• Hard max: 700–900 tokens
• Hard min: 120–150 tokens (unless the section is truly short)

Why this range works: it’s long enough to keep local context (definitions, conditions, exceptions), but short enough to retrieve precise pieces and fit several chunks into a model prompt.

When a section is huge (like a long policy under one heading), split it further by paragraphs or subtopics. When a section is tiny (like a one-line definition), attach it to the surrounding context, such as the heading plus the next paragraph.

Add overlap carefully (and only when it helps)

Overlap means repeating a small part of text from the end of one chunk at the start of the next. This helps when meaning crosses boundaries—common in narrative text and step-by-step instructions.

Guidelines:

• Typical overlap: 10–20% of chunk length (or 30–80 tokens)
• Use overlap when paragraphs reference prior context (“this”, “it”, “the above”).
• Reduce overlap for highly structured content (FAQs, dictionaries), where each unit is already self-contained.
• Watch duplicates: too much overlap can cause retrieval to return near-identical chunks, wasting top-k slots.

If your retrieval stack supports it, you can also handle continuity by retrieving adjacent chunks (“chunk expansion”) rather than overlapping everything up front.

Preserve context with smart headers and metadata

Chunks should carry enough context to be meaningful when shown alone. Two simple tactics raise quality sharply:

1) Prefix each chunk with its section path

Add a small header like:

• Document title
• H1 → H2 → H3 path
• Product/version (if relevant)

This “breadcrumb” text helps the embedding and gives the reader grounding. Keep it short; avoid repeating long boilerplate.

2) Store metadata for filtering

Attach metadata fields such as:

• Document type (policy, manual, release notes)
• Audience (admin, end user)
• Region or language
• Version/date
• Access level

Then your search can filter before similarity ranking, which improves precision and reduces irrelevant retrieval.

Treat lists, tables, and code as special cases

Lists

Lists often contain the exact answer users want. Splitting a list destroys it.

• Keep a list with its introductory sentence.
• If a list is very long, split into chunks by logical grouping (subheadings, categories), not by raw length.
• For numbered procedures, keep steps together where possible (e.g., steps 1–8 in one chunk) so the user gets a complete flow.

Tables

Tables can be tricky for embeddings and readability.

• Convert tables to a consistent text form (row-by-row or key-value).
• Chunk by table section or by a small group of rows that share a category.
• Include the table title and column headers in each table chunk, so rows remain interpretable.

Code blocks and configs

For developer docs:

• Keep code blocks with the paragraph that explains them.
• If a code block is long, consider splitting by functions or config sections.
• Store the language label and file path as metadata.

Use “atomic chunks” for FAQs and policies with exceptions

Some content is naturally atomic: one question plus one answer, one rule plus its exceptions, one definition plus scope notes.

For these:

• Chunk as a full unit: statement + conditions + exceptions + examples (if short)
• Avoid splitting exceptions into a separate chunk, since users often ask about edge cases (“Are there exceptions?”)

A useful pattern for policy text is:

• Rule statement
• Applicability (who/what it covers)
• Exceptions
• Enforcement or consequences
• Related terms

Validate chunking with retrieval tests, not gut feel

Chunking quality shows up in retrieval metrics and in real output.

Run a small evaluation set:

• 30–100 representative queries
• For each query, label which section should be retrieved

Then check:

• Hit rate @k: does the correct chunk appear in top 3 or top 5?
• Redundancy: are top results near duplicates due to overlap?
• Answerability: can a human answer using only the top retrieved chunks?

If you see “almost right” results, your chunks may be too large (burying the key sentence) or too small (missing context). Adjust size, overlap, and boundary rules accordingly.

Practical chunking recipes

Recipe 1: Product documentation

• Chunk by heading sections (H2/H3)
• Target 300–600 tokens
• Overlap 40–80 tokens
• Prefix with title + heading path
• Metadata: product, version, doc type

Recipe 2: Support articles and troubleshooting

• Keep symptom + cause + resolution together
• Target 400–800 tokens
• Use adjacency expansion instead of heavy overlap
• Tag with issue category and platform

Recipe 3: Contracts and policies

• Chunk by clause
• Keep definitions with their scope notes
• Target 250–500 tokens
• Metadata: jurisdiction, effective date, audience

Good chunking is less about one perfect number and more about consistent meaning-preserving splits, lightweight context, and testing against real queries. Start with semantic boundaries, tune size and overlap based on your document types, and let evaluation results guide refinements. When chunks read well on their own, AI search usually performs well too.