I’ve been building a RAG system for internal documentation, and our knowledge base is… let’s say ‘organically grown.’ Some docs are well-structured, some are PDFs that are basically scanned images, some are old wiki pages with broken links. It’s a mess.
Everybody in the literature talks about choosing between BM25, semantic search, or hybrid retrieval. But I’m wondering if any of that matters when your underlying data quality is already poor.
Is a sophisticated semantic retriever wasted on bad data? Or can a better retriever actually compensate for poor data quality by being smarter about finding relevant info even when the structure is inconsistent?
I’m also curious whether spending time cleaning up the knowledge base first is worth it compared to just picking a more robust retriever and accepting lower accuracy. There’s a real time tradeoff here, and I want to understand where the effort matters most.
Has anyone actually done this—built a RAG system with deliberately messy data to see where the performance breaks? Or is everyone’s advice always ‘clean your data first’ without actually testing the alternatives?
This is where Latenode’s access to multiple model options is honestly valuable. You can experiment without betting the farm.
Here’s what I’d do: deploy the same workflow with three different retrievers—keyword-based, semantic, and hybrid. Run your actual messy data through all three with sample questions you care about. See which one surfaces the most relevant documents despite the mess.
My guess is that semantic search will struggle more with malformed PDFs and inconsistent formatting, but it might compensate by understanding meaning even when structure is bad. Keyword search does better with weird formatting but misses conceptual relevance.
The real optimization isn’t necessarily picking the perfect retriever. It’s testing different approaches on YOUR data and iterating based on actual results, not theory.
Data cleaning is always worth doing, but you don’t have to wait for perfect data to find the right retriever. Start with what you have, measure performance, then decide if cleaning data or improving retrieval logic actually helps.
From what I’ve done with messy datasets, the honest answer is that bad data hurts more than a suboptimal retriever can fix. But you don’t need perfect data—you need data that’s consistently structured enough to be searchable.
What actually works is a two-phase approach. First, do minimal data cleanup—establish consistent formatting, extract text from PDFs properly, remove obvious junk. This doesn’t take forever and gets you 80% of the way there.
Then pick a hybrid or semantic retriever that can handle some sloppiness. These tend to be more forgiving of formatting inconsistencies than keyword search.
The trap is spending six months perfectly cleaning data when you’d learn more spending a week cleaning it ‘good enough’ and then testing retrieval performance. Iterate based on what your system actually returns, not what you think will work.
Data quality directly constrains retriever effectiveness. A sophisticated semantic retriever can’t compensate for fundamentally inaccessible information. However, the relationship isn’t linear. Minimal preprocessing—proper text extraction, removing corrupted sections, establishing basic structure—often yields disproportionate performance improvements. After that baseline cleanup, retriever sophistication matters more. Start with essential data cleaning, then optimize retriever selection, rather than pursuing perfect data or hoping retriever quality alone solves structural problems.
The quality floor matters more than the ceiling. Severely degraded data constrains even optimal retrievers. However, emphasis on perfect data cleanup before retriever selection often represents misallocated effort. A practical approach involves basic preprocessing to ensure extractability and searchability, then empirical testing of retriever approaches on your actual data. Performance gains from retriever optimization typically exceed diminishing returns from exhaustive data cleaning once baseline quality is established.