Core Specialization
RAG Applications
RAG (Retrieval-Augmented Generation) architectures combine Large Language Models with external vector knowledge sources to generate accurate, verifiable, and context-aware responses.
Context Accuracy & Advanced Data Pipelines
Retrieval-Augmented Generation optimizes LLM output by querying authoritative knowledge bases outside its training weights before generation, eliminating hallucinations and grounding responses in real organizational data.
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Advanced Ingestion & Parsing: Implementing semantic chunking, recursive splitters, and multi-modal document parsing for complex PDFs and unstructured data.
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Hybrid & Vector Search: Optimizing retrieval accuracy through dense vector search combined with BM25 keyword matching and cross-encoder re-ranking.
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Evaluation & Guardrails: Integrating evaluation frameworks and rigorous guardrails to continuously track retrieval correctness and factual precision.
Python
Weaviate
Pinecone
FAISS
LlamaIndex
LangChain
Cohere Rerank
ChromaDB
Case Study // File 02
Advanced RAG Application Pipeline
Built a production-grade, context-aware RAG pipeline engineered to ingest complex unstructured documentation and query them with absolute precision via hybrid retrieval and semantic chunking.
01. Document Ingestion & Parsing
Ingests multi-format unstructured documents using intelligent text parsers.
02. Semantic & Recursive Chunking
Breaks large source documents down into manageable, semantically coherent segments.
03. Vector Embedding & Indexing
Transforms text chunks into dense vectors indexed in high-performance vector databases.
04. Hybrid Retrieval & Re-ranking
Combines dense vector search with BM25 keyword matching and cross-encoder re-ranking.
Python
LangChain / LlamaIndex
Weaviate
OpenAI / Hugging Face
Cohere Rerank