A RAG chunk size calculator takes the guesswork out of one of the most impactful decisions in building retrieval-augmented generation pipelines. The right chunk size and overlap balance retrieval precision, context budget usage, and embedding quality — this tool computes the optimal configuration for your specific model, embedding model, document type, and retrieval setup.
LLM & Embedding Model
Total tokens in the document you want to index
Retrieval Configuration
Reserve space for your system prompt
RAG Configuration Recommendation
Optimized for your model, embedding, and retrieval setup
Context Budget Breakdown
Configuration Notes
Tip: These recommendations are starting points based on established RAG best practices. Always evaluate chunk size empirically with your specific dataset using retrieval metrics (MRR, NDCG, hit rate). Smaller chunks generally improve precision; larger chunks improve recall for complex queries.
How to Use the RAG Chunk Size Calculator
Chunking strategy is one of the most consequential decisions in building a RAG pipeline. Too small and your chunks lack context; too large and they dilute the semantic signal, exceed embedding model limits, or consume too much of your LLM's context window. This RAG chunk size calculator computes the right balance based on your specific configuration.
Step 1: Set Your LLM Context Window
Choose the model you plan to use for answer generation. The context window determines the total budget available for your system prompt, retrieved chunks, user query, and model response. A 128K model like GPT-4o can accommodate many more retrieved chunks than a 32K Mistral model, which directly impacts your optimal chunk size and top-k settings.
Step 2: Select Your Embedding Model
Your embedding model has its own token limit — for example, Cohere's embed-english-v3 supports only 512 tokens per chunk, while OpenAI's text-embedding-3-large supports up to 8,192. Your chunk size must stay within this limit. Smaller embedding models (512-token limit) force you to use smaller chunks, which can work well for precise retrieval but may lack context for complex queries. The calculator automatically constrains the recommended chunk size to the embedding model's maximum.
Step 3: Configure Document Type and Query Complexity
Document structure matters greatly for optimal chunk size. Conversational content and FAQs are already in short, self-contained units — smaller chunks (128–256 tokens) work best. Technical documentation benefits from medium chunks (256–512 tokens) that include enough context for a complete concept. Legal and research documents have dense cross-references and need larger chunks (512–1024 tokens). Code files should be chunked by function or class boundary rather than fixed token size. For query complexity: simple factoid queries need small precise chunks; complex analytical queries need larger chunks with more context per retrieved document.
Step 4: Set Retrieval Top-k
Top-k determines how many chunks are retrieved and inserted into the LLM context for each query. Higher top-k improves recall but consumes more context window space. With a 128K model and 512-token chunks, you could theoretically retrieve top-100, but this would flood the context and degrade answer quality. Typical production RAG systems use top-3 to top-10, with a reranker to filter down from a larger initial retrieval pool. The calculator shows the total context consumed by retrieved chunks at your chosen top-k.
Understanding Chunk Overlap
Chunk overlap ensures that sentences split at chunk boundaries are captured by at least one chunk in full. The recommended overlap is typically 10–20% of the chunk size. For a 512-token chunk, use 50–100 tokens of overlap. Overlap increases storage and embedding costs (because the total number of chunks increases), but significantly improves retrieval quality for content with long-running arguments or multi-sentence conclusions. The calculator shows both the overlap in tokens and as a percentage of the chunk size.
Context Budget Management
The context budget breakdown shows exactly how your LLM context is consumed across four components: system prompt (your instructions to the model), retrieved chunks (top-k × chunk size), estimated query size, and response buffer. A good target is to keep total input under 75% of the context window, leaving room for a thorough model response. If the budget is too tight, reduce top-k, reduce chunk size, or use a model with a larger context window.
For a detailed walkthrough, see our guide: RAG Architecture: Why Your Chunk Size Is Killing Retrieval Quality.
FAQ
Is this RAG chunk size calculator free to use?
Yes, completely free with no signup required. All calculations are done locally in your browser — no data is sent to any server. Use it as many times as you need to tune your RAG pipeline.
Is my data private when using this tool?
Absolutely. Everything runs in your browser with no network requests. No document content, configuration details, or results are transmitted anywhere. Your RAG architecture stays completely private.
What is the recommended chunk size for RAG?
For most RAG applications, 256–512 tokens per chunk works well for technical documentation. Conversational content and simple FAQs do better with 128–256 tokens. Legal and research documents with dense cross-references may benefit from larger chunks of 512–1024 tokens. This calculator tailors the recommendation to your specific model, embedding model, and retrieval setup.
What is chunk overlap in RAG and why does it matter?
Chunk overlap means adjacent chunks share some content at their boundaries. Without overlap, a sentence split across two chunks may be retrieved incompletely, causing poor answers. Overlaps of 10–20% of the chunk size are common — so a 512-token chunk might have a 64-token overlap. More overlap improves recall but increases storage and retrieval costs.
How does the embedding model affect chunk size?
Embedding models have their own token limits: text-embedding-3-small and -large support up to 8,192 tokens, while Cohere's models also support 512–4096 tokens depending on version. Your chunk size must not exceed the embedding model's limit. Larger chunks capture more context but may dilute the semantic signal, reducing retrieval precision.
What does top-k mean in RAG retrieval?
Top-k is the number of chunks retrieved from the vector database for each query. Retrieving more chunks (higher k) improves the chance of finding the right answer but uses more of the LLM's context window. For simple queries, top-3 or top-5 is usually sufficient. Complex queries may need top-10 to top-20, but this requires careful context budget management.
How does query complexity affect chunk size recommendations?
Simple queries (fact lookup, keyword-based) work well with small, precise chunks because the answer fits in a short excerpt. Complex queries (multi-step reasoning, synthesis across sources) need larger chunks to provide enough context. For complex queries, also consider using a reranker to filter from a larger initial retrieval pool down to the most relevant chunks.
What is the context budget breakdown in RAG?
A typical RAG context budget allocates: system prompt (500–1,500 tokens), user query (100–500 tokens), retrieved chunks (top-k × chunk size), and response buffer (500–2,000 tokens). The goal is to fit all retrieved chunks plus the system prompt and query within the model's context window while leaving sufficient space for the model to generate a thorough answer.