9 posts
The art of giving context: how much information should you give an AI model? Too little context yields incomplete answers, too much creates noise. A guide to relevant selection, ordering, and measurement.
Prompt engineering is now engineering, not art. Automated optimization with DSPy, an eval-driven workflow, structured output, prompt chaining, and Turkish-specific evaluation.
In 2026 prompt engineering isn't dying but evolving into context architecture. Context orchestration, adaptive prompting, workflow engineering, and Turkish-specific care.
As Gartner puts it, context engineering is in, prompt out. How the role evolved, patterns that still work (CoT, few-shot), and managing context at enterprise scale.
The era of hand-writing prompts is closing. I explain DSPy, meta-prompting and eval-driven loops that let the machine optimize your prompts.
Agentic RAG is not ordinary RAG. I explain the router, ReAct, plan-execute, multi-agent retrieval and self-RAG patterns, and when to choose each.
Even million-token windows lose the middle. Practical context management with the four pillars of context engineering, compression, RAPTOR, and memory systems.
AI is not destroying the value of professions; it is redistributing it. This evidence-based guide: which jobs AI augments rather than replaces, AI-core roles (AI engineer, agent builder), AI×domain hybrids (health, law, finance), resilient human-centric professions, the path from pressured roles to rising ones, the truth about 'prompt engineering is dead', Türkiye-specific opportunities, and a self-positioning framework. With Anthropic, WEF/LinkedIn, and Yale data.
Prompt engineering is dead, context engineering is alive. Anthropic's 90% cost-cutting prompt caching, GPT-5.5's 272K input threshold, Claude Opus 4.7's 1M context, and agent runtime state management are rewriting AI engineering in 2026. Turkish token efficiency, KVKK-compliant state stores, the 'Don't Break the Cache' principle.