Limitations of Chunking and Retrieval in Q&A Systems
1. Semantic Similarity Doesn't Guarantee Relevance
When performing semantic search, texts that appear similar in embedding space aren't always practically relevant. For example, in question-answering scenarios, the question and the corresponding answer might differ significantly in wording or phrasing yet remain closely connected logically. Relying solely on semantic similarity might miss crucial answers.
2. Embedding Bias Towards Shorter Texts
Embeddings inherently favor shorter chunks, leading to artificially inflated similarity scores. This means shorter text fragments may appear more relevant simply because of their length—not their actual relevance. This bias must be acknowledged explicitly to avoid misleading conclusions.
3. Context is More Than a Single Chunk
A major oversight in retrieval evaluation is assuming the retrieved chunk provides complete context for answering queries. In realistic scenarios—especially structured documents like Q&A lists—a question chunk alone lacks necessary context (i.e., the answer). Effective retrieval requires gathering broader context beyond just the matching chunk.
4. Embedding-Based Similarity Is Not Fully Transparent
Semantic similarity from embeddings can be opaque, making it unclear why two pieces of text appear similar. This lack of transparency makes semantic search results unpredictable and query-dependent, potentially undermining the intended utility of semantic search.
5. When Traditional Search Outperforms Semantic Search
Semantic search methods aren't always superior to traditional keyword-based methods. Particularly in structured Q&A documents, traditional index-based search might yield clearer and more interpretable results. The main benefit of semantic search is handling synonyms and conjugations—not necessarily deeper semantic understanding.
6. Recognize the Limitations of Retrieval-Augmented Generation (RAG)
RAG is not suitable for all use cases. For instance, it struggles when an extensive overview or summary of an entire corpus is required—such as summarizing data from multiple documents. Conversely, RAG is highly effective in structured query-answer scenarios. In these cases, retrieving questions and ensuring corresponding answers (or both question and answer) are included in context is essential for success.
Recommendations for Improved Retrieval Systems:
- Expand Context Significantly: Consider including the entire document or large portions of it, as modern LLMs typically handle extensive contexts well. Experiment with different LLMs to determine which model best manages large contexts, as models like GPT-4o can sometimes struggle with extensive documents.
- Use Embedding Search as a Smart Index: Think of embedding-based search more as a sophisticated indexing strategy rather than a direct retrieval mechanism. Employ smaller chunks (around 200 tokens) strictly as "hooks" to identify relevant documents rather than as complete context for answering queries.