Retrieval-Augmented Generation (RAG): Core Architecture
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Master the architectural RAG pipeline: document chunking, embedding, vector storage, context injection, and generation.
#ai-engineering#rag#embeddings#vector-db#interview-prep
The RAG Pipeline
Retrieval-Augmented Generation (RAG) updates LLMs with external, dynamic knowledge without needing expensive parameter training. The architecture runs in three stages:
- Ingestion: Loading enterprise documents, splitting them into logical chunks (e.g. 500 characters with 50-character overlap), and translating text to high-dimensional mathematical vector embeddings.
- Retrieval: When a user asks a question, the question is vectorized, and a vector database performs a similarity search to fetch the top-k most relevant chunks.
- Generation: The fetched text chunks are injected into the prompt context alongside the user query. The LLM references this context to generate a highly accurate, grounded answer.
RAG Pipeline System flow
[User Query] ---> [Embedding Model] ---> [Query Vector]
|
v
[LLM Generation] <--- [Prompt Context] <--- [Vector Database Search]
| (Query + Chunks)
v
[Grounded Response]Chunking Strategies & Overlaps
Example
When dividing documents, chunk sizes must be balanced. Chunks that are too small lack context, while chunks that are too large dilute semantic focus. • Recursive Character Chunking splits text dynamically by a list of separators (like paragraphs, newlines, and spaces), keeping sentences intact. • Chunk Overlap (e.g., 10%) ensures context continuity between boundary lines.
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