Applied MLSystems & Experiments
Exploring how explicit constraints and evaluation metrics can improve AI system reliability. Built and tested through real-world pipelines.
What Is Implemented Today
Smart-Trends Pipeline
Ingestion pipeline processing 5000+ articles daily with LLM-based enrichment, source attribution tracking, and structured evaluation outputs.
PostgreSQL + Vector Storage
Backend storage layer using PostgreSQL with pgvector for semantic retrieval and structured data management.
Constraint-Based Evaluation
Prototype evaluation method testing how explicit constraints (source attribution, factual grounding, uncertainty expression) affect output quality.
Limitations
- *This is an early-stage personal project, not a validated evaluation framework
- *No formal benchmarking or peer-reviewed results yet
- *Experimental and evolving system -- results are indicative, not definitive
- *Solo project with limited external validation
Engineering Focus
- →Real-world constraints: latency, cost, reliability
- →Pipeline-based evaluation instead of static theory
- →Emphasis on measurable outputs over conceptual claims
- →Built and tested through the Smart-Trends pipeline
Public work available on GitHub