This cheat sheet condenses the main engineering rules across modern LLM systems. It is intended as a practical companion to longer articles on prompting, RAG, fine-tuning, evaluation, observability...
This cheat sheet is a practical reference for designing LangGraph workflows that remain controlled, testable, and production-safe. LangGraph is most useful when the system is no longer a linear pro...
This cheat sheet is a practical reference for using LangChain in real systems. It is not a theory article. The goal is to help you make cleaner architectural choices, avoid common abstraction traps...
Generative AI systems inherit the risks of both software systems and language interfaces. They can misunderstand intent, follow malicious instructions, expose sensitive data, or trigger unsafe acti...
The next major step in generative AI is not simply larger text models. It is systems that can process and relate multiple modalities: text, images, audio, documents, screenshots, diagrams, and even...
Model quality matters, but in production the user experiences the serving system, not the research paper. A strong model with poor inference design becomes an expensive bottleneck. This is why infe...
LLM applications are difficult to debug when all you log is the final answer. By the time a user sees a bad response, the underlying cause may be several steps upstream: a bad retrieval result, an ...
Most LLM applications fail quietly before they fail visibly. They produce plausible answers, pass a few spot checks, and then underperform on real traffic because no serious evaluation framework wa...
Introduction Artificial intelligence is undergoing a fundamental transformation: we are transitioning from passive algorithms requiring constant supervision to autonomous agents capable of perceiv...
Introduction Language model alignment (LLM Alignment) has become one of the most critical challenges in modern artificial intelligence. A pre-trained language model like GPT-4, Claude, or Llama, h...