You can't just throw your data into a RAG pipeline and expect great results. How you break down your documents affects everything from retrieval accuracy to the coherence and usefulness of the generated responses. Poor chunking means the AI gets confused, misses context, or worse—answers inaccurately. Great chunking, on the other hand, means your model consistently delivers relevant, precise, and contextually complete responses.

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.

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.

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.