11d (edited) • In the News
Stop blaming the model for RAG failures 🛑
Most RAG systems are capped long before the model becomes the bottleneck. When a system quietly misses the one crucial passage in production, everyone tends to blame the model—but that gap between "nearly right" and "genuinely trustworthy" is almost never the model's fault.
In Part 1 of the new datapro.news series, we are breaking down why your AI's ceiling is actually set before a single prompt even runs. A language model can only reason over what retrieval hands it, meaning no amount of prompt cleverness can conjure back information that was lost three steps upstream.
Here are 4 open-source tools that we think change the game:
- 🕸️ Crawl4AI: Learn how to bypass web friction and turn messy, JavaScript-heavy sites directly into clean, LLM-ready Markdown.
- 📄 Marker: Discover how to process PDFs and documents without destroying their layout, keeping crucial financial tables and LaTeX equations perfectly intact.
- ✂️ Chonkie: See how to use lightning-fast semantic chunking so you store whole thoughts, rather than slicing vital sentences or definitions clean in half.
- 🗄️ Qdrant: Understand how to combine dense semantic vectors with exact lexical matching (hybrid search) so your database can accurately find specific SKUs, identifiers, and contract clauses.
Before you spend another week tuning your model, you need to look hard at what you are feeding it.
Check out the video edition below 👇
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Stop blaming the model for RAG failures 🛑
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