Curated by Raghu Challapilla
Production-ready AI applications solving real-world problems
Inspectors spend 1–3 hours writing inspection reports. A multimodal, multi-agent RAG system analyzes voice notes and photos to generate InterNACHI/ASHI-compliant inspection reports. Includes a domain-grounded assistant for inspection standards, codes, and guidelines.
Built for the Bank of America Innovation Challenge, this RAG-based assistant validated that domain knowledge onboarding can be done in days rather than months with scripted NLU bots.
Built a conversation AI to explore ethical alignment in autonomous systems integrating cross domain reasoning from philosophy and machine ethics
Technical demos covering advanced AI engineering topics
Production-ready AI with security guardrails and 2-3x faster responses through intelligent caching strategies.
Implementing Google's A2A protocol enabling independent agents from different vendors to discover and collaborate.
Building modular, deployable agent platforms with instant model/tool swapping using LangGraph Studio.
Deep dive into Model Context Protocol (MCP) - the standard for LLM clients to discover and utilize external tools and data.
Cost-effective AI research application using modular graph-based pipelines to produce high-quality outcomes efficiently.
Implementing 6 retrieval strategies: BM25, multi-query, contextual compression, parent document, ensemble, and semantic chunking.
Building robust evaluation frameworks for RAG and agentic applications using RAGAS with synthetic test data generation.
Creating synthetic test data for RAG pipeline evaluation using LangSmith for comprehensive quality assurance.
Building sophisticated multi-agent systems using LangGraph for complex orchestration and coordination.
Creating intelligent agents capable of multi-step reasoning and multi-tool coordination using LangGraph.
Using LangSmith for monitoring performance, evaluating correctness, tracking latency, and debugging AI applications.
Complete RAG system implementation using LangChain, LangGraph, LangSmith, LCEL, and Python.
Product Leader with hands-on AI engineering skills delivering applied AI systems at enterprise scale