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Venkata Bayarapuneni
Junior Full-Stack Developer specializing in JavaScript, Python, and cloud-deployed web apps
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Built and deployed a production LLM-powered travel assistant (Globe Trote) that automates end-to-end trip planning using a multi-step agent pipeline with RAG, external API calls, and enforced structured JSON outputs. Focused on reliability (validation, retries, fallback prompts, logging) and reported a ~30–40% reduction in irrelevant/generic responses after adding retrieval grounding.
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Key Strengths
- Shipped an end-to-end LLM travel-planning agent to production on AWS EC2
- Improved relevance by adding RAG (estimated 30–40% reduction in irrelevant/generic outputs in testing)
- Made LLM outputs predictable via structured JSON, reducing formatting errors for frontend consumption
- Designed multi-step agent workflow with orchestration/state passing across intent extraction, retrieval, API calls, and generation
- Reliability engineering for LLM systems: input validation, retries, fallback prompts, and logging/monitoring
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