Intermediate · 6 articles · MODULE 5: RAG SYSTEMS
Tutorial · Intermediate · 21 min
A retrieval-augmented system can't compare a question against a whole document at once, so the document is split into overlapping chunks that get embedded and searched independently.
Includes Colab notebook
By: Anacodic Team
Start Reading →Tutorial · Intermediate · 26 min
A vector database stores one embedding per chunk and finds the nearest ones — but an index is only meaningful for vectors produced by the model that built it.
By: Anacodic Team
Start Reading →Tutorial · Intermediate · 26 min
Everything downstream of ingestion can only work with text you actually put in the index, and the three decisions that cap the system all fail silently.
By: Anacodic Team
Start Reading →Tutorial · Intermediate · 26 min
Retrieval hands you a ranked list; generation has to turn it into an answer someone can check — and the prompt wording is rarely what decides that.
By: Anacodic Team
Start Reading →Tutorial · Intermediate · 22 min
Chunking decides what retrieval is able to return, so it caps everything downstream. The metric is not size but whether a rule survives intact.
By: Anacodic Team
Start Reading →Tutorial · Intermediate · 23 min
Lexical and dense retrievers fail on different queries, which is why hybrid search exists. Fusing them has a trap worth knowing about.
By: Anacodic Team
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