Andrej Karpathy supports the introduction of a new term related to "context engineering" in AI Software development using LLMs. And this term has long seemed very necessary. Every time I explain to people how we develop our Nethermind AuditAgent, one of the key aspects, besides using domain expertise (web3 security) and using the best available AI models (from OpenAI, Anthropic and Google), and tools for LLM, is precisely "context engineering". There's sometimes an expression "context is the king," and it really is true. LLMs, whether huge advanced ones or optimized small LLMs, are a powerful tool, but like any tool, if it's in the wrong hands, you'll get much less promising results than you could if you work with them correctly. And context management (or engineering) is indeed a complex and not very well-described area that is constantly evolving, and it really emerged as an extension of the concept of prompt engineering, which already has some negative connotations. Overall, Andrej listed the main aspects related to context engineering (on the second screenshot), but in each specific task, people achieve excellent results largely through trial and error, each time monotonously trying to select the right context elements that are really needed at this stage of problem-solving, collecting benchmarks for each stage, looking at metrics, dividing datasets into test, validation, and so on, and so forth. What do you think about "context engineering"?