Multi-modal RAG: Text Isn't Enough Anymore
Why your RAG system needs to ingest more than just text to deliver real value.
Blog · Topic
RAG & Knowledge Systems
Retrieval that holds up in production: chunking, embeddings, hybrid search, reranking, metadata and index freshness.
14 posts
Graph Databases for Complex Relationships
Why vector embeddings fail when entity relationships matter, and how adding Knowledge Graphs to your RAG pipeline fixes context fragmentation.
Semantic Caching: The Missing Layer in Your RAG Architecture
Why hitting the model API for every query is burning your budget and slowing down your users.
The Similarity Score You're Trusting Blindly
Most teams pick a similarity threshold once during dev and never look at it again. That number is making decisions for you every day.
The Hybrid Search You Keep Putting Off
Vector search alone isn't retrieval. It's a starting point. The teams shipping accurate RAG systems know the difference.
The Index You Forgot to Rebuild
Your RAG pipeline retrieved the right answer six months ago. The source doc changed. Nobody re-indexed it.
The Metadata You Forgot to Index
You built a RAG system that retrieves semantically. Metadata filtering isn't an optimization — it's the difference between a search engine and a lucky guess.
The Query Rewriter You're Not Using
Most RAG systems retrieve against the user's raw query. That's the problem. Query rewriting is the highest-leverage improvement most teams skip entirely.
The Embedding Model You Chose in Week One
You picked an embedding model early, it worked well enough, and you never looked at it again. That's the problem.
Reranking Is Not Optional
Similarity scores aren't relevance scores. A cross-encoder reranker is the highest-leverage fix most RAG pipelines are missing.
Your RAG Pipeline Needs Monitoring, Not Just Better Retrieval
Tuning chunk size and tweaking similarity thresholds won't save you when your pipeline silently degrades in production.
Knowledge Base Maintenance Is a Product, Not a Project
You spent three months building the RAG knowledge base. Then you shipped it and moved on. That's why it's already wrong.
RAG Evaluation Frameworks: Beyond 'Does It Look Right?'
Vibes-based RAG evaluation is how you ship broken retrieval to production. Here's what a real eval framework looks like.
Chunking Strategies That Actually Affect Retrieval Quality
Most RAG pipelines fail at chunk size 512, split by character, never revisited. Here's what improves retrieval — and why your defaults are probably wrong.