Generative AI is about teaching machines to create — transforming language, data, and logic into new artifacts: text, code, visuals, or insights. It’s not just automation; it’s the computational side of imagination.

At genai.arunprasher.dev, the focus is on the engineering layer behind these systems — exploring how large language models (LLMs), retrieval-augmented generation (RAG), embeddings, and fine-tuning can be built into practical, production-ready workflows.

Each experiment here investigates real implementation details: prompt design, vector search pipelines, context caching, model inference optimization, and system orchestration. The goal is to move from theory to code — showing how modern applications can integrate LLMs intelligently and efficiently.

All posts and prototypes are open-sourced on  GitHub , documenting lessons learned while merging full-stack development with generative intelligence.