Enterprise RAG Document Q&A Agent

Build a Retrieval-Augmented Generation engine parsing documentation, chunking text, computing vector embeddings, and answering user queries.

0 of 2 milestones complete

Engineer a end-to-end RAG architecture with vector similarity search (cosine distance), semantic chunking, and LLM context synthesis.

### Project Overview
Engineer a RAG document search agent using vector embeddings and LLM context synthesis.

Estimated Time⏱️ ~6 Hours

Difficulty📊 intermediate

Milestone Count📋 2 Milestones

Text ChunkingVector EmbeddingsCosine Similarity SearchRAG Context Injection

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INTERMEDIATE PROJECTNOT STARTED ○

Build a Retrieval-Augmented Generation engine parsing documentation, chunking text, computing vector embeddings, and answering user queries.

0 of 2 milestones complete

Engineer a end-to-end RAG architecture with vector similarity search (cosine distance), semantic chunking, and LLM context synthesis.

### Project Overview
Engineer a RAG document search agent using vector embeddings and LLM context synthesis.

Estimated Time⏱️ ~6 Hours

Difficulty📊 intermediate

Milestone Count📋 2 Milestones

Text ChunkingVector EmbeddingsCosine Similarity SearchRAG Context Injection