Absolutely — for a RAG pipeline, embeddings and chunking matter a lot more than people often expect. The “best” choice depends on your content type, query style, latency budget, and whether you need multilingual support.

Quick recommendation

If you want a strong default:

• Embeddings:

  • OpenAI text-embedding-3-large for highest quality
  • OpenAI text-embedding-3-small for cheaper/faster and often good enough
  • If self-hosting: bge-large-en-v1.5 or e5-large-v2 for English
  • For multilingual: bge-m3 is a strong option

• Chunking strategy:

  • Start with semantic-ish chunks around 300–800 tokens
  • Use 10–20% overlap if the text is narrative or contains definitions/procedures
  • Prefer structure-aware chunking: split by headings, paragraphs, bullet lists, code blocks, etc.
  • Avoid arbitrary fixed-size splitting unless the data is very uniform

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How to choose embeddings

1) Consider your primary retrieval style

If your queries are short and factual

Examples:

• “What is the refund policy?”
• “How do I rotate logs?”
• “What is the SLA for enterprise customers?”

Use embeddings that do well on semantic similarity and short-query matching:

• OpenAI text-embedding-3-large
• bge-large-en-v1.5
• e5-large-v2

If your content is technical or code-heavy

You may want:

• A model that handles exact terminology well
• Potentially hybrid search with BM25 + embeddings

Good options:

• bge-large-en-v1.5
• e5-large-v2
• bge-m3 if multilingual/heterogeneous corpus

If your corpus is multilingual

Use a multilingual model rather than translating everything:

• bge-m3
• intfloat/multilingual-e5-large
• OpenAI embeddings also handle multilingual reasonably well, but dedicated multilingual models are often stronger for retrieval in non-English corpora

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2) Tradeoff: quality vs cost vs latency

Best quality

• text-embedding-3-large
• bge-large-en-v1.5

Better cost/performance

• text-embedding-3-small
• bge-base-en-v1.5
• e5-base-v2

Self-hosting considerations

If you self-host embeddings:

• GPU/CPU inference cost
• Throughput
• Index rebuild time
• Dimensionality and vector store cost

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3) Use one embedding model for retrieval, not multiple unless needed

Don’t overcomplicate at first. Pick one strong retriever embedding model and evaluate.

A common stack:

• Dense embeddings for semantic retrieval
• BM25 keyword retrieval
• Optional reranker on top

This is often better than trying to use multiple embedding models at once.

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Chunking strategy: what works best

1) Chunk by structure first

Best practice is to split along:

• Markdown headings
• HTML sections
• PDF paragraphs
• Document sections/subsections
• Bullet lists
• Code blocks separately

Why this helps:

• Keeps related information together
• Makes retrieved context more coherent
• Reduces “orphaned” fragments

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2) Recommended chunk sizes

A good starting range:

• Narrative/docs/help content: 300–600 tokens
• Technical docs / policies / manuals: 400–800 tokens
• FAQ-style content: often one Q/A per chunk, or slightly larger if answer spans multiple paragraphs
• Code/docs: chunk by function/class/module boundaries when possible

Overlap

• 10–20% overlap is usually enough
• Example: 500-token chunks with 50–100 token overlap

Too much overlap:

• Increases index size
• Creates near-duplicate chunks
• Can hurt diversity in retrieval results

Too little overlap:

• Risks splitting key context across chunks

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3) Avoid purely fixed-size chunking unless necessary

Fixed-size chunking can split:

• A definition from its explanation
• A question from its answer
• A procedure step from its exception
• A code function from its docstring

Use fixed-size chunking only as a fallback.

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4) Parent-child chunking is often excellent

This is a strong RAG pattern:

• Child chunks: small chunks embedded and retrieved
• Parent chunks: larger surrounding sections returned to the LLM

Benefits:

• High recall from small chunks
• Good context from larger chunks

Example:

• Embed 200–400 token chunks
• Store a mapping to 1,000–2,000 token parent sections
• Retrieve small chunks, then expand to parent section for generation

This works especially well for:

• Policies
• Technical documentation
• Legal content
• Long articles

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Best chunking approach by content type

Documentation / knowledge base

Use:

• Heading-based splitting
• Paragraph-based splitting
• 400–800 token chunks
• 10–15% overlap

FAQs

Use:

• One question-answer per chunk
• Keep each answer intact if possible
• Add metadata with the question/topic

PDFs

PDFs are tricky because layout can be messy. Use:

• Layout-aware parsing
• Section/paragraph reconstruction
• Remove headers/footers
• Chunk by logical sections, not page breaks

Code repositories

Use:

• Function/class-level chunking
• Include surrounding docstrings/comments
• Possibly store file path, symbol name, and language metadata

Legal/policy docs

Use:

• Clause/section-based chunking
• Preserve numbering and headings
• Prefer smaller chunks with parent expansion

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Retrieval quality tips beyond embeddings and chunking

1) Add metadata

Store metadata like:

• document title
• section heading
• URL/path
• timestamp/version
• language
• source type

This improves filtering and answer grounding.

2) Use hybrid retrieval

Dense embeddings are great, but keyword search helps with:

• exact names
• product IDs
• error codes
• legal terms
• acronyms

A strong baseline:

• BM25 + dense vectors
• Then rerank top results

3) Add a reranker if you can

A reranker can dramatically improve top-k quality.

Pipeline:

1. Retrieve top 20–50 with embeddings/BM25
2. Rerank with a cross-encoder or LLM-based reranker
3. Pass top 3–8 chunks to the generation model

This often matters more than squeezing the last bit of performance from embeddings.

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Practical starting configs

Option A: Strong general-purpose RAG

• Embeddings: text-embedding-3-large
• Chunking: 500 tokens, 75 token overlap
• Split on headings/paragraphs first
• Hybrid retrieval + reranker

Option B: Lower-cost production setup

• Embeddings: text-embedding-3-small
• Chunking: 400–600 tokens, 50–75 token overlap
• Hybrid retrieval
• Reranker if budget allows

Option C: Self-hosted open-source English

• Embeddings: bge-large-en-v1.5 or e5-large-v2
• Chunking: structure-aware, 400–700 tokens
• Hybrid retrieval + reranker

Option D: Multilingual

• Embeddings: bge-m3
• Chunking: structure-aware, 400–700 tokens
• Hybrid retrieval
• Reranker with multilingual support

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How to evaluate

Don’t choose blindly. Test on a small labeled set.

Measure:

• Recall@k: does the correct chunk appear in top k?
• MRR / nDCG: how high is the right chunk ranked?
• Answer accuracy: does final response actually answer correctly?
• Citation precision: are retrieved chunks relevant and well-grounded?

Create 20–100 real queries and inspect:

• retrieved chunks
• whether the answer is contained
• whether chunk boundaries are helping or hurting

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A simple rule of thumb

If you’re unsure:

• Use structure-aware chunks
• Start around 500 tokens
• Overlap ~75 tokens
• Use text-embedding-3-large or bge-large-en-v1.5
• Add hybrid retrieval
• Add a reranker

That’s a very solid baseline.

If you want, I can also help you choose based on your exact setup:

• document type
• language(s)
• expected query types
• vector DB
• latency/cost constraints

If you share those, I can recommend a concrete embedding model + chunking configuration.