AI Engineering — RAG, Agentic AI & LangChain
AI Engineering — RAG, Agentic AI & LangChain
The Problem
AI is moving fast. Most teams struggle to go from prototype to production with LLMs — dealing with hallucinations, latency, cost control, and reliable tool use.
What I Deliver
RAG Pipelines
- Document ingestion, chunking, and embedding pipelines
- Vector search with OpenSearch, Pinecone, or pgvector
- Hybrid search (semantic + keyword) for accuracy
- Citation tracking and source attribution
Agentic AI
- LangChain and LangGraph agent workflows
- Multi-step reasoning with tool use
- Human-in-the-loop approval flows
- Error recovery and retry strategies
LLM Integration
- AWS Bedrock (Claude, Titan) and OpenAI (GPT-4)
- Prompt engineering and optimization
- Cost management — model routing, caching, batching
- Evaluation and observability for production AI
Full-Stack
- React, TypeScript, Node.js applications
- React Native mobile apps
- Event-driven architectures (SQS, SNS, Lambda)
Typical Results
- Production-ready AI in weeks, not months
- 50%+ cost reduction with batch processing and model routing
- Measurable accuracy through evaluation pipelines
How It Works
- Discovery — Understand your use case and data
- Prototype — Working proof of concept in 1-2 weeks
- Production — Scale, optimize, and harden for real users
- Iterate — Continuous improvement based on metrics