Testing a 100,000-document repository revealed that the most basic vector search returned a completely irrelevant cloud-native article as its top result—we believe "how to search" determines the success or failure of RAG (Retrieval-Augmented Generation) projects far more than merely "having a database."
What this is
We note that many enterprises build knowledge bases, yet their AI still hallucinates. The reason is often not a dumb model, but an overly simplistic "data retrieval" strategy.
The most basic current approach is "similarity search" (fetching if the semantics match), but facing massive documents, it has three fatal flaws: duplicate results (5 articles explaining the exact same concept), low-quality mixing (forcing Go language concurrency onto someone asking about Python), and ignoring explicit conditions (the user asks for "2024," but it brings back 2022 results as well).
Consequently, the industry is shifting toward more complex retrieval strategy combinations: MMR (Maximal Marginal Relevance, balancing relevance and result diversity to avoid clustering), threshold filtering (setting a minimum relevance score, preferring fewer results over padding with filler), and Self-Query (having AI automatically decompose user queries, converting "2024" into a hard database filter condition). Moving from pure semantic comparison to balancing diversity and hard conditions is the necessary path for AI retrieval to evolve from "barely usable" to "highly effective."
Industry view
We judge that the competitive focus of RAG has shifted from "using a vector database" to the refinement of "retrieval engineering." Everyone realizes that without good strategies, more data is just noise.
However, this is not without controversy. Some architects point out that Self-Query relies heavily on the LLM's ability to decompose user intent; if the model misinterprets the conditions (e.g., misclassifying "Apple phone" as a fruit category), it can result in empty knowledge base queries. Furthermore, combining multiple retrieval strategies significantly increases system response latency. The sharp rise in engineering complexity is locking out small and mid-sized teams lacking technical depth.
Impact on regular people
For enterprise IT: The focus of knowledge base construction shifts from "hoarding data volume" to "optimizing the retrieval pipeline"; merely purchasing a vector database can no longer solve business pain points.
For the workplace: When querying AI, explicitly providing qualifiers like time and category better triggers Self-Query functionality, yielding precise answers.
For the consumer market: C-end AI products will no longer ramble or veer off-topic; users will noticeably feel that the materials AI provides emphasize diversity and accuracy.