AI & LLM Engineering · By yonatangross

LLM integration patterns for function calling, streaming responses, local inference with Ollama, and fine-tuning customization.

yonatangross/orchestkit

Performance optimization patterns covering Core Web Vitals, React render optimization, lazy loading, image optimization, backend profiling, LLM inference, and sustainability UX.

yonatangross/orchestkit

Retrieval-Augmented Generation patterns for grounded LLM responses.

yonatangross/orchestkit

Run isolated eval and grading calls using CC 2.1.81 --bare mode.

yonatangross/orchestkit

Evals-first error analysis for LLM apps: clusters real Langfuse or JSONL traces into a human-confirmed failure taxonomy with counts, then recommends binary pass/fail evals for recurring named modes.

yonatangross/orchestkit

LLM and AI testing patterns — mock responses, evaluation with DeepEval/RAGAS, structured output validation, and agentic test patterns (generator, healer, planner).

yonatangross/orchestkit

LangGraph 1.x (LTS) Python workflow patterns for state management, delta channels, resilience (node timeouts, error handlers, graceful drain), routing, parallel execution, supervisor-worker, tool…

yonatangross/orchestkit