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Sruthi Kondapalli
Intern Software Engineer specializing in backend systems and Generative AI
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Built and deployed a scalable, production-ready LLM knowledge assistant using a RAG architecture (LangChain + vector store/FAISS) to replace keyword search for internal documents. Demonstrates hands-on expertise in hallucination reduction and retrieval quality improvements through semantic chunking, similarity tuning, prompt design, and human-in-the-loop validation, plus strong stakeholder communication via demos and visual explanations.
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Key Strengths
- Built and deployed a production RAG-based LLM knowledge assistant for internal document Q&A
- Deep practical focus on reducing hallucinations via prompting, retrieval tuning, and confidence rules
- Strong retrieval quality optimization (semantic chunking, chunk overlap/size experiments, similarity tuning)
- Modular AI workflow design with clear success criteria and measurable evaluation (latency/accuracy/relevance)
- Structured testing approach (unit + end-to-end + benchmark queries) for LLM workflows
- Effective collaboration with non-technical stakeholders using demos, diagrams, dashboards, and iterative feedback
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