Browsing: Engineering

Prompt engineering interview questions will help the interviewer assess your skill level and likelihood of success in the role.

For the last two years, people have learned to get better results from large language models (LLMs) by writing clearer prompts: add context, define the role, give examples, specify the format, and iterate. This is still useful. But as AI moves from chatbots to coding agents, research assistants, data science copilots, and autonomous workflows, “good…

The programme aims to teach professionals to use generative AI tools effectively, safely and responsibly at work.

The difference between an impactful and a worthless AI response often comes down to the prompts you provide.

is built in three engineering layers stacked on a single LLM call. Prompt engineering is the call itself: the system message, the instructions, the schema that fixes the output shape. Context engineering is what fills the model’s window: retrieval, compression, deciding what to keep out. Loop engineering is what happens around the call: when the…

variable rename passes Git, greenlights your mocked test suite, and gets approved in code review—right up until it crashes every real production call the moment it executes.

In the first week of June 2026, a two-sentence post on X from Peter Steinberger, creator of OpenClaw and engineer at OpenAI,accumulated millions of viewsand split the developer community. The post urged engineers to stop prompting AI coding agents manually and start designing systems that prompt the agents on their behalf. Days later, Google Cloud…

The user asks “what is the effective date of this policy?”. Retrieval returns five candidate line-windows and passes them all to the LLM in one prompt. The LLM reads all five to extract the same date the first candidate already had. Chunks two to five were signatures, footnotes, and a paragraph about historical dates. Paid…

Or are you still struggling with answers that only get more tangled, like a ball of yarn?

Context engineering is the practice of designing everything a language model sees before it generates a response.