Blog
Post-mortem style technical breakdowns explaining why systems were built, key design trade-offs (Latency vs. Accuracy, Spatial vs. Frequency, Guardrails vs. Retries), bottleneck fixes, and core takeaways.
From Prompts to Graphs: The Evolution of AI Application Engineering
How AI engineering moved from controlling what a model says to controlling how an entire system behaves.
As AI applications matured beyond single-prompt chatbots, developers faced new challenges: handling missing knowledge, taking real-world actions, retrying failed steps, coordinating multi-agent teams, and enforcing deterministic boundaries. This article traces the 5-stage evolutionary progression from Prompt Engineering (2022) to Graph Engineering (2026).
How Systems Like ChatGPT Manage Long Conversations Efficiently
An engineering analysis of how modern LLM applications manage long-running conversations using context windows, retrieval, summarization, and inference optimizations without exhausting GPU infrastructure.
Large Language Models are fundamentally stateless, yet applications like ChatGPT create the experience of persistent conversation. This case study explores how modern LLM systems combine sliding context windows, retrieval, summarization, persistent memory, KV caching, and efficient inference techniques to balance latency, infrastructure cost, and response quality.