Prompt & Context Engineering

Clear goal, relevant context, explicit constraints, the desired output format and — when useful — examples. It should be specific enough that a smart new colleague could follow it.

Including a few input–output examples in the prompt so the model copies the pattern, format and style.

Deciding everything the model sees at each step — instructions, retrieved documents, tool results, memory and examples — and keeping it relevant and compact so the model performs reliably.

Test it on a fixed evaluation set with clear scoring before and after, rather than judging from one or two examples.

Ground the model with retrieved sources, ask it to cite them, allow it to say 'I don't know', lower temperature for factual tasks and verify outputs.

Splitting a complex task into a sequence of smaller prompts where each output feeds the next, which improves reliability and makes debugging easier.

An attack where text in user input or retrieved documents tries to override your instructions. Mitigate by separating data from instructions, limiting tool permissions and validating outputs.

When you need a consistent style or format at scale, have many good examples, and prompting alone is too long, costly or inconsistent.

Randomness of the output. Low values give focused, repeatable answers; higher values give more varied, creative ones.

Short hands-on sessions with real tasks, a shared prompt library, clear rules on confidential data, and a habit of verifying outputs before use.