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Varshitha Pendyala
Mid-level Generative AI Engineer specializing in LLMs, RAG, and agentic systems
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Built a production "Mini RAG Assistant" for internal document Q&A, focusing on grounded answers (anti-hallucination), retrieval quality, and latency/cost optimization. Uses LangChain/LangGraph for orchestration and applies a metrics-driven evaluation loop (including reranking and semantic chunking improvements) while collaborating closely with product stakeholders.
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Key Strengths
- Built and deployed a production document QA RAG assistant over internal docs
- Improved retrieval quality via semantic chunking with overlap
- Reduced latency and cost through caching embeddings and repeated retrieval queries
- Mitigated hallucinations using strict context-grounded prompting
- Designed and orchestrated RAG/agent workflows using LangChain and LangGraph
- Reliability-focused evaluation using accuracy/latency/cost/failure-rate metrics
- Iterative optimization based on evaluation findings (added reranking for multi-part questions)
- Effective collaboration with non-technical stakeholders (PM) via trade-off communication and prototyping
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