Technical guides and step-by-step instructions on building cloud-native applications, engineering thought leadership, and scaling Agentic AI.
Build an enterprise extraction pipeline that assesses its own confidence, flags ambiguous edge cases, and intelligently routes requests between automated processing and human review.
Why asking an LLM for JSON fails in production, and how to use forced tool calling and response schemas to guarantee valid, type-safe structured output every time.
Transform your LLM from a passive text generator into an active agent. Learn how to declare external tools with JSON schemas and parse function calls in the Google GenAI SDK.
Move beyond casual text prompts. Learn the principles of structured prompt engineering with XML boundary tags, explicit constraints, and prompt injection defense.
Build robust evaluation pipelines for AI systems. Implement exact-match test suites, schema validators, and the LLM-as-a-judge pattern to grade output quality and policy compliance.
Master sampling parameters in the Gemini API. Understand temperature tradeoffs between determinism and creativity, utilize stop sequences, and inspect finish reasons.