Eventum’s Guide to Mastering RAG: Chunking Done Right

David Bressler, PhD

May 18, 2025

At Eventum, we've spent countless hours optimizing Retrieval-Augmented Generation (RAG) systems, and we've learned one critical thing: chunking is absolutely essential to getting it right. It might sound trivial—just breaking documents into smaller bits—but chunking fundamentally shapes the quality, accuracy, and reliability of your AI’s responses. Here's what we've found matters most when tackling chunking for RAG.

Why Chunking is Crucial (Trust Us!)

Master Retrieval-Augmented Generation chunking with Eventum’s expert guide, optimizing AI data retrieval and boosting effectiveness in your AI solutions.

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The Core Factors to Get Chunking Right

At Eventum, when we start tuning a client's RAG system, these are our go-to considerations:

Optimal Chunk Size: Typically, chunks of a few hundred tokens work best. Too small, and you lose context; too large, and retrieval becomes messy and inefficient.
Semantic Coherence: We always aim to keep logical or topical coherence intact, ensuring each chunk can meaningfully stand alone.
Overlap (Sometimes!): Slight overlaps between chunks can preserve critical context, but too much overlap introduces unnecessary redundancy and overhead.
Computational Trade-Offs: Yes, more sophisticated methods perform better, but they're not free. The trick is finding the sweet spot between quality and computational load.

Chunking Techniques: From Basic to Cutting-Edge

Fixed-Length Chunking

Simple and fast: splits documents uniformly.
Easy to implement but often too simplistic for nuanced information.

Semantic Chunking

Uses linguistic structures (sentences, paragraphs) to keep meaningful context.
Better retrieval accuracy but more resource-intensive.

Contextual Chunking (Our Current Favorite)

Popularized by Anthropic, adds explanatory context within chunks.
This massively improves standalone chunk clarity and dramatically reduces retrieval errors.

Hierarchical and Summarization-Based Chunking

Breaks down content into layered granularities, allowing multi-level retrieval—from broad context down to specific details.
Perfect for structured, lengthy documents.

LLM-Assisted Agentic Chunking

Uses large language models themselves to intelligently determine optimal chunk boundaries.
Adaptive, highly effective, but computationally intensive. Ideal for high-stakes, precision-critical tasks.

Our Advice: There’s No One-Size-Fits-All

After extensive hands-on experience at Eventum, we confidently say there’s no single best chunking strategy for every situation. Instead, here's how we approach it:

Evaluate Your Content: Structured texts might call for hierarchical approaches; more diverse or complex documents could benefit from contextual or LLM-driven chunking.
Balance Computational Costs: For rapid, less-critical applications, simpler methods are fine. For precision-sensitive cases, invest in advanced chunking.
Iterate and Improve: Don't rely on default chunking out-of-the-box. Real-world performance needs tuning, testing, and refinement based on direct feedback and results.

Expertise Makes All the Difference

Here's the reality: naive chunking rarely delivers optimal RAG results. At Eventum, we often step into projects where out-of-the-box solutions haven't lived up to expectations. Why? Because effective chunking needs expertise and careful tailoring to your specific data, domain, and objectives.

At Eventum.ai, we specialize in exactly that—expertly optimizing RAG pipelines with tailored chunking strategies. Our seasoned ML engineers work closely with you to select, implement, and refine chunking methods, transforming your generative AI projects into powerful, reliable business tools.

If you want RAG done right, let's talk. Eventum has you covered.

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