AI Engineering Lab
Structured learning path from fundamentals to production AI systems
This is my AI engineering learning journey made tangible. Each project represents a deep dive into production AI patterns—from vector embeddings and multi-agent systems to cost optimization, security guardrails, and observability. Built with OpenAI, Claude, LangChain, and Next.js.
Why build this? Because the best way to learn AI engineering is to build production-grade AI products. Each demo is fully functional, well-documented, and demonstrates enterprise-ready patterns that actually ship to production.
Experimental
Live projects & prototypes
Embedding Visualizer
Visualize 53 portfolio items in semantic space using PCA. Query against my experience to find closest matches.
RAG Playground
Compare 4 RAG strategies side-by-side with real metrics. Discover why Basic RAG wins on small knowledge bases, when Hybrid adds value, and re-ranking trade-offs.
In Development
Research, discovery & planning phase
Career Matcher
Multi-agent system that analyzes job descriptions and maps them to my experience using structured outputs.
Prompt Playground
Zero-shot vs few-shot comparison, prompt injection demos, and cost optimization calculator.
On the Horizon
Ideas taking shape
Fine-Tuning Lab
Q1A/B test base vs fine-tuned models. Includes data synthesis pipeline and cost-benefit analysis.
Inference Optimizer
Q1Interactive cost calculator showing 50-70% savings through caching, batching, and quantization.
Guardrails & Safety
Q2Model router, PII detection, fallback strategies, and content filtering. Enterprise-ready reliability.
AI Observability
Q2Real-time metrics dashboard: quality scores, cost tracking, latency monitoring, and A/B testing.
Analytics RAG: Numbers vs Words
Q3Compare semantic search (documents) vs deterministic retrieval (analytics). Explore metadata-guided data access using real CMS hospital data. Inspired by enterprise AI architecture patterns.
Inspired by: Sandhiya Vignesh (Principal Product Manager, Atlassian)
Three-layer architecture pattern for analytics AI systems
What I'm Learning
Constantly exploring new frontiers in AI, ML, and product development
Fundamentals
Engineering
Product & Strategy
Architecture & Decisions
This AI Lab uses a hybrid architecture designed for optimal performance and cost. Rather than defaulting to a single AI provider, I strategically selected different models for different use cases.
Embedding Visualizer
AI Assistant (Portfolio-wide)
RAG Playground
PM Insight:
Different AI tasks require different models. Using the right tool for each job optimizes for quality, speed, and cost. This is the kind of architectural decision-making that separates strategic PMs from feature factory executors.
Tech Stack
Tools & technologies powering the lab