Selecting the Best Embedding Method for Limited Domain Knowledge

By Alex P. Wang  |  May 21, 2025  |  Tags: RAG, Embedding, Chunking, Query-Focused, Hybrid Approach

Retrieval-Augmented Generation (RAG) is a powerful framework that combines semantic search with language model generation. In a typical RAG setup, a user query is embedded into a vector space and matched against a set of pre-embedded content chunks stored in a vector database. These relevant chunks are then passed to a language model to generate an informed, contextual response. But how those content chunks are created - through chunking strategies - plays a critical role in determining the precision, latency, and quality of retrieval. At the same time, a fundamentally different strategy known as query-focused embedding offers an alternative: instead of chunking documents, it embeds anticipated user queries and links them directly to structured answers. This approach is particularly effective in private-domain RAG systems where questions and answers are known, structured, and limited in scope. This article is divided into two parts. First, we explore 15 chunking strategies used in real-world RAG applications. Then we compare them to the query-focused embedding method to help you determine which approach best fits your private-domain knowledge system.

Chunking Methods

1. Fixed-Length Chunking

How it works: Fixed-length chunking divides text into equally sized segments based on a token or character count - for example, 500 tokens per chunk. This method ignores sentence or paragraph boundaries and treats the content as a continuous stream.

Use case: Simple, scalable baseline for indexing large volumes of unstructured text.

Illustration:

Original Text (excerpt):

    Welcome to our hotel. Breakfast is served daily from 6:30 AM to 10:00 AM. Free Wi-Fi is available in all rooms. Please contact the front desk for late check-out options. Pool hours are 8:00 AM to 10:00 PM.
    

Fixed-Length Chunking (100-token chunks):

• Chunk 1: Welcome to our hotel. Breakfast is served daily from 6:30 AM to 10:00 AM. Free Wi-Fi is available...
• Chunk 2: ...in all rooms. Please contact the front desk for late check-out options. Pool hours are 8:00 AM...
• Chunk 3: ...to 10:00 PM.

Note: Chunk boundaries may split mid-sentence or mid-idea.

Common Fixed-Length Chunk Sizes (Tokens): The optimal chunk size depends on your content type, embedding model, and performance goals.

Pros

Cons

2. Sentence-Boundary Chunking

How it works: Chunks are split at natural sentence boundaries using NLP libraries (like spaCy or NLTK). Each sentence becomes its own retrievable unit, ensuring that the chunk holds a complete and coherent thought.

Use case: Works well for FAQs, email replies, and chatbot scripts.

Pros

Cons

3. Paragraph-Level Chunking

How it works: This method treats each paragraph as a semantically complete chunk. Paragraph boundaries are typically preserved from the source document's formatting (e.g., newlines or indentation). It assumes that each paragraph contains a logically cohesive idea or answer segment. While Paragraph-Level Chunking sounds ideal in theory, it's often not feasible in practice - especially when paragraphs are long or inconsistently formatted. A single paragraph can easily exceed the token limit of embedding models, making direct embedding impossible without truncation or loss. Recursive Text Splitting solves this by trying to preserve full paragraphs when possible, but gracefully degrades to sentence- or word-level splits when necessary.

Use case: Structured documents such as manuals, SOPs, or web articles.

Pros

Cons

4. Sliding Window Chunking

How it works: Sliding window chunking is an extension of fixed-length chunking that adds controlled overlap between consecutive chunks. It creates overlapping segments by shifting a fixed-size window (e.g., 512 tokens) across the text with a set overlap size (e.g., 256 tokens). This helps maintain contextual continuity across chunk boundaries and avoids splitting meaningful content mid-thought. A common overlap size is 20% to 50% of the chunk length - typically 128 to 256 tokens for a 512-token chunk.

Use case: Best for transcripts, legal documents, or any scenario where ideas span across multiple sentences or paragraphs.

Pros

Cons

5. Recursive Text Splitting

How it works: Text is recursively split by structure (headings → paragraphs → sentences) until it fits within a token limit. Recursive Text Splitting breaks text using a prioritized list of delimiters - starting with higher-level structures like paragraphs and falling back to smaller units like sentences, words, or characters only when necessary. This ensures that chunks stay within a specified token limit while preserving as much semantic structure as possible. In practice, this method behaves like a smart, adaptive version of paragraph- and sentence-level chunking. Instead of committing to one fixed level, it tries to keep content in larger, coherent blocks (like paragraphs), and only splits further (into sentences or words) if the chunk would otherwise exceed the token limit. This makes it both flexible and production-friendly - ideal for handling real-world content of varying lengths.

Use case: Ideal for books, reports, and structured wikis.

Illustration:

Delimiter hierarchy (fallback order):

["\n\n", "\n", ".", " ", ""]

Input Text:

## Welcome Guide

Breakfast is available from 6:30 - 10:00. Free Wi-Fi is available. Please contact the front desk for assistance.

Recursive Split Steps:

1. Try splitting by double newline → produces one section
2. If too long, split by newline
3. Then by period (.) for sentences
4. Then by space or individual characters if necessary

Recommended Thresholds for Recursive Splitting

Pros

Cons

6. Semantic-Aware Chunking

How it works: Semantic-aware chunking uses natural language understanding to split content into meaningful units based on topic shifts, entity boundaries, or latent discourse structure. Instead of relying on characters or tokens, it leverages tools like sentence transformers, topic models, or embeddings to identify where one idea ends and another begins. Some implementations use similarity thresholds: sentences are grouped until the similarity between the next sentence and the current group falls below a cutoff.

Use case: Ideal for knowledge-intensive domains, such as technical documentation, legal clauses, or research papers, where topical boundaries are subtle but important.

Illustration:

Original Text:

    “Welcome to our hotel. Breakfast is served daily from 6:30 AM to 10:00 AM. 
    Free Wi-Fi is available in all rooms. Please contact the front desk for late check-out options.”
    

A semantic-aware chunker might group:

• Chunk 1: Welcome message + breakfast
• Chunk 2: Wi-Fi info + check-out instructions

Reason: The chunker detects a semantic shift between hospitality-related content and logistical instructions.

Pros

Cons

7. Structure-Aware Chunking

How it works: Structure-aware chunking uses the underlying formatting of documents - such as HTML tags, Markdown headers, list items, or table structures - to define chunk boundaries. It respects visual and organizational hierarchy (e.g., <h2>, <p>, <li>) and typically aligns chunks with logical blocks like sections or components.

Use case: Web scraping, API documentation, codebases.

Pros

Cons

8. Graph-Based Chunking

How it works: Graph-based chunking models the document as a graph of text units (e.g., sentences or paragraphs) connected by semantic similarity. It then applies community detection or graph partitioning algorithms (like Louvain or Spectral clustering) to form clusters of closely related nodes, which become the final chunks.

Use case: This method is especially useful when content is loosely structured or nonlinear - such as notes, interviews, or research summaries - where paragraph or heading boundaries are unreliable.

Illustration:

• Nodes: Sentences from the document
• Edges: Weighted by cosine similarity between sentence embeddings
• Chunking: Apply graph clustering → each cluster = one chunk

Pros

Cons

9. Visual Layout-Aware Chunking

How it works: Visual layout-aware chunking uses cues from document layout - such as columns, tables, text boxes, font size, spacing, and indentation—to define chunk boundaries. This method often involves rendering PDFs or HTML and analyzing the document's visual structure using tools like pdfplumber, Unstructured.io, or OCR frameworks. Rather than splitting text by token count or grammar, it preserves the structure readers see on screen, improving the interpretability and usability of results.

Use case: Great for insurance forms, product brochures, multi-column PDFs, and corporate reports.

Illustration

Example layout:

+----------------------+----------------------+
| Hotel Amenities      | Room Policies        |
| - Free Wi-Fi         | - Check-out: 12 PM   |
| - Fitness Center     | - No pets allowed    |
+----------------------+----------------------+

Chunk 1: Left column (Hotel Amenities)

Chunk 2: Right column (Room Policies)

Note: These chunks reflect the visual sections, not sentence order.

Pros

Cons

10. Query-Aware Chunking

How it works: Query-Aware Chunking = Paragraph-Level Embeddings + Sentence-Level Filtering at Retrieval Time. Instead of relying solely on pre-chunked static units, Query-Aware Chunking first retrieves coarsely pre-chunked content (typically at the paragraph or section level), and then dynamically re-chunks or filters those results at query time. This is done using techniques like sentence scoring, passage ranking, or extractive summarization to identify the most relevant span for the user query.

Use case: Open-ended search with minimal preprocessing.

Pros

Cons

11. LLM-Driven Segmentation

How it works: LLM-driven segmentation uses a language model (like GPT-4 or Claude) to analyze a document and break it into semantically coherent chunks. Instead of relying on token counts or hard-coded delimiters, the LLM is prompted to "understand" the structure and suggest logical split points based on meaning, tone shifts, headings, or topic boundaries.

Use case: Long narratives, instructional content.

Pros

Cons

12. Metadata-Aware Chunking

How it works: Metadata-aware chunking incorporates structured metadata—such as titles, tags, authorship, timestamps, categories, or source IDs—into the chunking and retrieval process. While the chunking itself might follow standard methods (e.g., paragraph-level), the association of metadatawith each chunk enables more intelligent filtering, grouping, or routing at retrieval time.

Use case: Meeting notes, customer support chat logs.

Pros

Cons

13. Event-Based Chunking

How it works: Event-based chunking segments content by discrete events or actions, rather than by structure or tokens. It's especially useful in logs, transcripts, timelines, or procedural narratives where each event is a meaningful unit of information.

Use case: News reports, journals, user behavior logs.

Pros

Cons

14. Compression-Based Chunking

How it works: Compression-based chunking uses extractive summarization or sentence selection techniques to create condensed chunks that contain only the most salient information. Rather than splitting documents into equal parts, this method scores sentences based on importance (e.g., using a transformer-based model) and selects the top ones to form a compressed, information-dense chunk. This is especially helpful for long documents that cannot be processed in full due to token limits or cost constraints.

Use case: Legal contracts, multi-page documents.

Pros

Cons

15. Hybrid Chunking

How it works: Hybrid chunking combines two or more chunking strategies to take advantage of their respective strengths. For example, a system might use paragraph-level chunking for coarse indexing, and then apply query-aware filtering or compression-based summarization on the retrieved content at query time.

Use case: Best for large-scale or production RAG systems where user queries vary widely and a single chunking method is insufficient.

Query-Focused Embedding Method

While traditional RAG systems rely on retrieving chunks of source documents, an alternative approach - Query-Focused Embedding - takes a fundamentally different path. Instead of embedding long passages of content and hoping for a good match, this method embeds the questions themselves (or likely user queries), and directly links each one to a concise, curated answer.

This strategy is especially effective in private, limited-domain systems where:

• The types of user questions are predictable (e.g., hotel policies, product FAQs, cooking stories)
• The answers are structured or short-form
• Precision and speed are more important than open-ended exploration

Rather than slicing documents into hundreds of retrievable chunks, you create targeted embeddings like:

• "What time is breakfast?" 🡒 "Breakfast is served from 6:30 AM to 10:00 AM."
• "Is Wi-Fi free?" 🡒 "Yes, free Wi-Fi is available in all rooms."
• "Where did Alex get the smoked salmon?" 🡒 "The smoked salmon came from Alaska, adding a smoky depth to the dish."
• "Is there a story behind Alex's risotto?" 🡒 "Yes, the risotto was inspired by a trip to Milan and built from ingredients found spontaneously in the fridge."

Strengths of Query-Focused Embedding

• High Precision: Directly maps user queries to known answers with minimal ambiguity.
• Low Latency: Smaller vector index means faster lookups and response times.
• Controlled Outputs: Answers are pre-written and curated, reducing hallucinations.
• Multilingual Friendly: Embed multiple phrasings across different languages to the same answer.
• Ideal for Predictable Domains: Works well for FAQs, hospitality, and structured support systems.

Tradeoffs of Query-Focused Embedding

• Limited Generalization: Struggles with unseen or unexpected queries unless augmented.
• Authoring Overhead: Requires writing and maintaining query variants and answer mappings.
• Lower Recall on Unknowns: Cannot answer outside the curated query set without a fallback.

Strengths of Chunk-Based Strategies

• Broad Coverage: Any document content becomes searchable, without predefining questions.
• Good for Long-Form Content: Suitable for articles, documentation, and unstructured knowledge.
• Lower Setup Effort: Less manual effort required in authoring queries.

Tradeoffs of Chunk-Based Strategies

• Lower Precision: May retrieve partial or irrelevant chunks depending on query and chunk quality.
• Chunking Matters: Retrieval quality heavily depends on how well content is segmented.
• Scalability Challenges: Larger indexes result in slower similarity search and higher costs.

When to Use Each

Best of Both Worlds: The Hybrid Approach

In many real-world RAG systems - especially in structured but exploratory domains like personal knowledge bases, cooking stories, or internal support tools - a hybrid strategy often provides the best of both worlds.

How it works: The system attempts to match a user query using query-focused embeddings first. If the result is highly confident (e.g., based on a similarity threshold), it returns the linked answer card directly. If no match is found, or confidence is low, the system falls back to chunk-based retrieval, searching across semantically indexed text chunks for broader context.

This layered approach allows for:

• Precision when possible: Fast, clean answers for known queries.
• Exploration when needed: Semantic chunk search for creative or unexpected questions.
• Fallback coverage: Ensures the user always gets a relevant result, even outside the curated set.

For example, in a cooking story assistant, a query like “What dishes use clams?” could be answered immediately using query-focused embeddings. But a question like “Tell me a story about Alex’s most creative seafood dish” would benefit from retrieving full story chunks and passing them to a language model for synthesis.

This hybrid setup is increasingly used in production systems where both structure and storytelling coexist.

Vector Database Setup

A practical and effective hybrid RAG setup typically uses two separate vector databases:

How It Works

1. User submits a query
2. System searches the Query-Focused DB first
3. If match score is high → return curated answer
4. If not → fallback to Chunk-Based DB, retrieve top-k chunks, and send to LLM

Benefits of Keeping Them Separate

• Each DB is optimized for its own purpose (precision vs. coverage)
• Independent tuning of thresholds, index sizes, and update frequency
• Easier to monitor, debug, and scale

Optional: One DB with Filters

You can store both types of vectors in the same database and add a source tag like \"query\" or \"chunk\". Then use filter-based retrieval. However, this adds complexity and removes some of the tuning flexibility offered by separate indexes.

Conclusion

Choosing the right embedding strategy is one of the most impactful decisions when building a Retrieval-Augmented Generation (RAG) system in a limited-domain context.

In this article, we explored 15 chunking methods - each with unique strengths, tradeoffs, and ideal use cases. We then compared these approaches to query-focused embedding, a powerful alternative that excels in structured, predictable environments where precision and speed matter most.

For many private-domain applications, such as hotel guest assistants or personal cooking knowledge bases, the best solution is often a hybrid: query-focused embeddings for known intents and curated answers, combined with chunk-based retrieval for exploratory or long-form content.

By thoughtfully combining these methods and understanding their differences, you can build more accurate, responsive, and trustworthy RAG systems that scale with both structure and nuance.