Introduction
In a world where AI retrieval systems (RAG) are becoming increasingly complex, it's easy to get lost in the intricacies of embeddings, vector databases, and reranking pipelines. Yet often, the end user simply wants an answer to a straightforward question like "How do I reset my password?" So, why complicate matters?
Understanding Decision Factors
Before diving into RAG architectures, it's crucial to understand when and why to use each approach. The key decision factors include:
- Data Freshness: Real-time updates favor approaches with easy re-indexing. Daily or weekly updates work well with hybrid approaches. A stable corpus with monthly or quarterly updates justifies using pre-embeddings.
- Corpus Characteristics: High churn (more than 10% changes daily) means you should avoid full pre-embedding. Stable documents work well with pre-embedding. Long-tail distribution (90% never accessed) means on-the-fly approaches win.
- Query Patterns: Keyword-heavy queries should start with full-text search. Semantic or conversational queries benefit from embeddings. Mixed patterns need hybrid approaches.
- Scale & Performance: Less than 1000 queries per day means simple approaches are sufficient. 1K to 10K queries per day requires selective optimization. More than 10K queries per day justifies full optimization.
- Team Capabilities: No ML expertise means stay with full-text plus query rewriting. Some ML experience makes hybrid search manageable. Having an ML team available makes advanced approaches viable.
Architecture 1: MVP – Full-Text Search Only
What It Is
Good old BM25. Elasticsearch. Postgres full-text search. Solutions that existed before "embedding" became a verb.
When to Use
Just starting out? Your users write keyword-style queries ("pandas merge dataframe")? Exact matches matter ("invoice #12345")? You want zero ML complexity? Your corpus has proprietary terminology.
Pros
Zero API costs. Fast (under 10ms). Easy to debug (you can see exactly why a document matched). Surprisingly effective (handles many use cases). No chunking strategy needed – works with full documents. No evaluation complexity – easy to test and validate. No model deprecation risk (BM25 doesn’t change).
Cons
Misses synonyms ("car" vs "automobile"). Fails on semantic queries ("How do I...?"). Can’t understand intent beyond keywords.
Real Talk
In my experience, this handles a significant portion of use cases. Don’t skip this step. You might be surprised how far you can get.
Conclusion
No need to over-engineer to effectively meet end-users' needs. Choose the right RAG architecture based on your specific needs and optimize based on real data.
Let's discuss your project in 15 minutes.