LLM, RAG & Agentic AI

What it covers

Design LLM applications with retrieval, prompt workflows, and agent orchestration. Capabilities include RAG pipelines, LangChain/LangGraph flows, Google ADK multi-agent systems, custom MCP servers, LiteLLM routing, and Langfuse observability — aligned with current Agentic AI engineering work.

Business value

Move from chat demos to operable AI systems that retrieve context, take actions, and stay observable.

Features

• RAG pipelines with vector databases
• Prompt engineering and evaluation loops
• Multi-agent orchestration
• Custom MCP server integration
• Observability with Langfuse

Process

• Workflow mapping — Identify retrieval needs, tools, agents, and guardrails for the use case.
• Agent & RAG build — Implement embeddings, tools, orchestration, and evaluation checkpoints.
• Harden for operations — Add API layer, Redis/runtime glue, and observability for reliable runs.

Technologies

• RAG
• LangChain
• LangGraph
• Google ADK
• LiteLLM
• MCP
• Pinecone
• FAISS
• ChromaDB
• FastAPI
• Redis
• Langfuse