Day 5: Why Chunk Size, Embeddings, and Search Strategy Matter in RAG

Retrieval-Augmented Generation (RAG) systems blend large language models with a tailored knowledge base of your data. If you're a product manager or business lead, think of RAG as giving your AI assistant a curated reference library for its answers. But getting RAG right means paying attention to three key ingredients:

1. How you chunk your data

2. The quality of embeddings (those magical vector representations of text)

3. Your similarity search strategy (for fetching information)

Let's unpack each in simple terms and see how they impact your system's performance and accuracy.

Chunking: Finding the "Just Right" Size

Chunking is the practice of splitting a long text (or any large data object) into smaller chunks before you embed, store and feed it into an LLM. Think of it as cutting a 500-page manual into bite-sized cards so search and reasoning can stay focused. Done well, chunking boosts retrieval accuracy, keeps latency predictable, and lets you update a corpus without reprocessing the entire thing.

Chunk size definitely matters. Too large a chunk and you end up with a vector that is too vague (it dilutes specific info in a sea of text). Too tiny a chunk and you lose context (like trying to understand a movie from a single frame). The goal is to find the right chunk size that is neither too big nor too small.

Strategies for smart chunking: Many teams start with fixed-size chunks (say 500 or 1000 characters/tokens) for simplicity. This works okay for uniform text like news articles, but it may cut off important sentences. You can also use variable chunks that respect natural boundaries (paragraphs, sections), which keeps ideas intact but results in uneven chunk sizes. A common best practice is to use overlapping chunks: a sliding window where each new chunk repeats a bit of the previous chunk's end. This 10–20% overlap ensures no important context is lost between chunks. However, the tradeoff is that you end up storing some duplicate text and slightly boost your storage and retrieval load. But overlap can significantly improve retrieval accuracy by catching edge-case details that might otherwise be missed.

Of course, there isn't a one-size-fits-all, perfect chunk size. It depends on your content and queries. If users ask very detailed questions (What's the exact error code description on page 5?), smaller chunks that is focused on fine details work better. For broad questions (Summarize this 20-page report), larger chunks preserve the narrative. Since it's a perpetual balancing act between context and precision, the key is to test and iterate. Start with a reasonable chunk length (many use a few hundred words) and adjust based on results. RAG systems often require this kind of tuning to hit the sweet spot.

Embedding Space

If chunks are the pieces of our knowledge puzzle, embeddings are how we represent each piece in a way the AI can understand. An embedding is a learned vector of numbers that places an object (text, image, audio and etc.) in a continuous space where semantically similar items lie close together. You can picture embedding space like a map of meanings: texts about similar topics cluster together. Similar chunks end up with vectors that point in similar directions in this high-dimensional space. For example, an embedding model might place "financial earnings" close to "quarterly revenue" even if they don't share keywords. This what makes embeddings powerful since it captures conceptual similarity beyond exact wording .

However, not all embeddings are created equal. The phrase "garbage in, garbage out" applies here: the quality of your embeddings directly affects what your RAG system retrieves. A well-trained, domain-specific embedding model will group related information tightly together, making it easy to find the right chunk. But a poor-quality or generic embedding model might scatter related info all over the map. Your system might retrieve chunks that are only vaguely relevant, or miss the best answer entirely. Research shows that domain-tuned embeddings significantly outperform generic ones, boosting recall by double-digit percentages in tests.

Another aspect of embedding quality is dimensionality and training. Since an embedding space is multi-axis map of meanings, the number of dimensions represents the number axises there are. More dimensions give the model extra axises to store the nuances and let it carve finer boundaries between concepts. Training, on the other hand, teaches the model where to place each vector on this multi-dimensial map of meanings. Classic word embeddings like Word2Vec and GloVe find patterns so that, for example, "king" ends up near "queen" in the vector space. Today's sentence embeddings do this for whole chunks of text. They capture nuances like differentiating bank (financial) from bank (river) based on context.

That said, even the best embeddings have limitations. They excel at semantic similarity like finding conceptual similarities, but they might not grasp things like document structure or the importance of a single keyword. For example, an embedding might tell you two documents are conceptually related to "pharmaceuticals", but it won't inherently know one contains the specific drug name a user asked for. Since embeddings tend to favor theme over detail, If a user searches for a precise fact, a pure semantic search could retrieve something broadly relevant but miss the exact detail . This is where our next topic comes in – how we actually search those embeddings.

Similarity Search Strategy

Once your data is chunked and embedded, the RAG system needs to search for which chunks best answer a user's question. How it performs this similarity search can make a big difference in results. There are a few strategies to know, even if the terms sound technical:

• Semantic Vector Search (Dense): This is the bread-and-butter of modern AI search. The query is turned into an embedding, and the system finds the closest vector neighbors (i.e. the chunks most semantically similar). This method shines at catching paraphrases or concept matches (asking "How to prevent overheating?" will find content about "cooling down a system," even if the wording differs). However, as noted, it might overlook exact keywords or rare terms since it cares about overall meaning, not exact wording.
• Lexical Search (Sparse): Think of old-school keyword methods like BM25. They look for overlapping words and give high scores when there's an exact hit on an unusual term. Lexical search is great when specificity counts: if you search an error code or a proper name, a BM25-based search will zero in on that exact string like a metal detector finding a coin. It doesn't understand meaning, though. If wording differs ("NYC" vs "New York City"), a pure lexical search can miss the connection unless both terms appear.
• Hybrid Search: Why not both? Hybrid search combines semantic and lexical approaches to get the best of both worlds. In practice, this might mean retrieving results from both a vector index and a keyword index, then merging them, or using a specialized hybrid index that stores both dense and sparse representations together. The idea is simple: a dense database casts a wide net based on meaning while a sparse database search ensures precision on the exact terms. Hybrid methods have become popular because they can catch cases that pure semantic search might miss (for example, a specific legal term or a product ID). This is especially useful if your data has a lot of unique identifiers, technical terms, or structured info embedded in text.

Under the hood, implementing a good similarity search often involves using specialized vector databases and indexes. Techniques like HNSW (Hierarchical Navigable Small World graphs) allow extremely fast nearest-neighbor searches among millions of vectors with very high recall (often 95%+ of relevant items can be found). This means even at scale your system can quickly grab the most similar chunks without breaking a sweat. But even the best ANN (approximate nearest neighbor) index can sometimes fetch a couple of less-relevant stragglers. That's why some RAG setups add a reranking step: essentially a second-pass filter (often a more precise but slower model, like a cross-encoder or even another LLM) that fine-tunes the ordering of the retrieved chunks. Rerankers act like a fact-checker, ensuring the top results truly answer the query. They can be especially handy when you need high precision (say for legal or medical answers), though they add some latency.

Conclusion

• Chunk Size Sweet Spot: Break documents into chunks that are large enough to preserve context but small enough to be specific. Avoid "overstuffed" chunks that muddle meanings or ultra-tiny chunks that lose context. Aim for a happy medium aligned with your LLM's context window and the detail level of user queries. Remember, chunk size directly influences what comes up in search.

• Use Overlap (Don't Lose Context): If important info might fall between chunks, use a sliding window with a bit of overlap (commonly 10–20% of the chunk length). This overlap acts like a safety net to catch cutoff sentences or ideas, boosting retrieval accuracy (users won't miss an answer just because it straddled two chunks). The trade-off is a bit more storage and redundancy, but it's usually worth it for better results.

• Invest in Quality Embeddings: The better your embedding model understands your domain, the better your RAG system will perform. Domain-tuned or high-quality embeddings can double the recall of relevant info compared to generic ones. This ensures related info is clustered together in the vector space, so relevant chunks aren't missed due to vocabulary differences.

• Efficient Vector Search Matters: Use a robust vector index (like HNSW graphs or similar) to search embeddings quickly and thoroughly. Modern indexes can retrieve results with over 95% recall in milliseconds even with millions of entries. This means your system won't keep users waiting, and it won't leave relevant knowledge on the table. In practice, it's wise to pair vector search with metadata filters (e.g., by document type or source) to narrow the field when you know the context—this speeds things up and improves relevance.

• Embrace Hybrid Search: Don't rely on semantic search alone. Combining semantic vectors with keyword-based search (hybrid search) catches both the meaning and the exact terms . This is especially useful for pinpointing factual or numerical answers that pure semantic search might overlook. A hybrid approach ensures that if a user's query contains a rare term or a specific phrase, the system gives it due weight—all while still understanding the query's intent. In short, a semantic and lexical combo improves both recall and precision for your answers.

• Test, Tune, and Reiterate:

RAG systems are not set-and-forget. Continuously monitor how well retrieval is working (e.g., what percentage of answers find the correct source chunks) and adjust accordingly. If you update your data or switch to a new embedding model, re-embed your chunks so the vector store stays accurate. Use user feedback or sample queries to spot issues. Iteration is key; even small tweaks in chunking strategy or search parameters can notably boost your system's accuracy and user satisfaction.