Press enter or click to view image in full size
Photo by Gemini
Member-only story
4 Retrieval Strategies for Building a Robust, Production-Ready RAG System
11 min read6 hours ago
–
Retriever is the heart of any Rag based Systsem, and also the most critical point of failure too.
Most RAG systems fail at retrieval, not generation.
You can use the best language model available, but if your retrieval pulls the wrong documents, you get wrong answers. It’s that simple.
In this blog ill tell you 3 retrieval strategies with production optimizations at the end in plain, simple words, and by the end, you will be able to make a more robust Rag System like an architect.
let’s go
Why Retrieval Is Everything
When people build RAG systems, they obsess over prompts and model choice…
Press enter or click to view image in full size
Photo by Gemini
Member-only story
4 Retrieval Strategies for Building a Robust, Production-Ready RAG System
11 min read6 hours ago
–
Retriever is the heart of any Rag based Systsem, and also the most critical point of failure too.
Most RAG systems fail at retrieval, not generation.
You can use the best language model available, but if your retrieval pulls the wrong documents, you get wrong answers. It’s that simple.
In this blog ill tell you 3 retrieval strategies with production optimizations at the end in plain, simple words, and by the end, you will be able to make a more robust Rag System like an architect.
let’s go
Why Retrieval Is Everything
When people build RAG systems, they obsess over prompts and model choice. They test GPT-5 versus Claude.
They tweak temperature settings. They add system messages. But the real problem happens earlier.
The Retrieval step determines what information the model sees.
If it retrieves irrelevant chunks, the model has nothing good to work with. It will generate confident-sounding nonsense.