RAG Architecture

Understands RAG fundamentals: document chunking, embedding generation, vector search, and context injection. Sets up basic RAG pipelines using LangChain or LlamaIndex for product Q&A features.

Understands RAG pipeline components: embedding models, vector databases, and retrieval strategies. Implements basic document indexing and semantic search with FAISS or Pinecone for LLM augmentation.

Knows RAG basics: retrieval, augmentation, generation. Applies simple RAG pipelines for NLP tasks: searching relevant documents and generating context-based answers.

Builds production RAG systems with hybrid search (dense + sparse retrieval), re-ranking, and metadata filtering. Implements evaluation frameworks measuring retrieval relevance and answer faithfulness for user-facing features.

Implements advanced RAG patterns: query decomposition, hypothetical document embeddings (HyDE), and multi-index retrieval. Optimizes chunk size and overlap for domain-specific corpora and evaluates retrieval quality with RAGAS metrics.

Independently designs RAG systems for NLP: hybrid search, reranking, query expansion. Configures chunking strategies for different document types, evaluates quality via RAGAS.

Designs enterprise RAG architecture with multi-tenant knowledge bases, access control integration, and real-time document ingestion. Implements agentic RAG with iterative retrieval and self-correcting answer generation for complex product workflows.

Designs scalable RAG platforms with graph-augmented retrieval, contextual compression, and adaptive chunking strategies. Implements knowledge graph integration for structured reasoning and mentors team on RAG evaluation methodology and hallucination mitigation.

Designs production RAG architectures: multi-index retrieval, agentic RAG, self-reflective RAG. Optimizes quality through advanced reranking, query decomposition, and citation verification.

Defines RAG Architecture strategy at the team/product level. Establishes standards and best practices. Conducts reviews.

Defines RAG Architecture strategy at team/product level. Establishes standards and best practices. Conducts reviews.

Defines RAG strategy for the NLP team. Establishes evaluation framework, retrieval and generation best practices, and quality assurance standards for RAG-based NLP products.

Defines RAG Architecture strategy at the organizational level. Establishes enterprise approaches. Mentors leads and architects.

Defines RAG Architecture strategy at organizational level. Establishes enterprise approaches. Mentors leads and architects.

Shapes enterprise RAG strategy for the NLP platform. Defines architectural patterns, knowledge management standards, and retrieval infrastructure at organizational level.