AI Application APIs
FastAPI, REST endpoints, request validation, and structured error handling.
Private repository · In progress
AI Systems Engineering — In Progress
Extending my LLM Systems Lab into a modular, deployable AI application, integrating RAG, API development, containerization, workflow orchestration, and agentic AI.
Status and scope. This is a current, privately maintained project. The capabilities below describe the intended engineering direction, not a verified list of completed or production-deployed features. The public LLM Systems Lab documents the existing RAG foundations separately.
Technical roadmap
Planned capabilities
Containerization & Deployment
Docker, environment-based configuration, Docker Compose, and multi-service application deployment.
Workflow Orchestration & Automation
n8n, webhooks, API integrations, and automated AI workflows.
Agentic AI
Tool-using LLMs, multi-agent workflows, and Model Context Protocol (MCP) integrations.
Reliability & Evaluation
Automated testing, retrieval and response evaluation, logging, and failure handling.
Engineering Practices
Modular Python architecture, Git-based development, CI/CD, and reproducible environments.
Proposed technology stack
From a working RAG lab toward an application.
Core stack (planned): Python · FastAPI · RAG · Docker · Docker Compose · n8n · LLM APIs · Agentic AI · MCP · GitHub Actions.
The private repository is not available for public review, so implementation details, delivery dates, benchmarks, and deployment outcomes are intentionally not asserted. The public RAG foundation is linked below.
View public LLM Systems Lab ↗