RAG Pipeline

Clean corpora and embeddings only matter if the retrieval pipeline is engineered correctly. I design RAG stacks that balance precision, recall, and performance — ensuring that context retrieval isn’t just fast, but also relevant and trustworthy.

My Approach

• Retriever Configuration – Adaptive top-k, Maximal Marginal Relevance (MMR), and metadata filters to control both recall and diversity.

• Reader Integration – LLMs prompted with structured context windows, citation mode enabled, and fallback handling when confidence is low.

• Reranking – Optional cross-encoder rerankers to sharpen results beyond the initial vector search.

• Citations & Transparency – Every answer ties back to its source passage, making outputs explainable and auditable.

Advancing Further

I continue to evolve methodology toward:

• Dynamic Retrieval – query-aware context length and adaptive k-values to optimize accuracy vs cost.

• Rerank at Scale – experimenting with lightweight rerankers to balance latency with higher semantic precision.

• Multi-Stage Retrieval – combining hybrid search (keyword + semantic) with domain classifiers for specialized knowledge routing.

Why It Matters

LLMs without grounding hallucinate. With a properly engineered RAG pipeline, I ensure responses are not only accurate and context-aligned but also verifiable against original sources — a non-negotiable for enterprise and mission-critical environments.