Why "a good prompt" alone isn't enough anymore
A single, well-written prompt is fine for a one-off chat request. Once AI runs productively inside a process - say, a support tool or an internal automation - what matters is more than the single text: the system instruction that applies for the whole session, the expected response format, and what information even makes it into the context in the first place. That whole interplay is called "context engineering".
System prompt vs. user prompt
A system prompt sets role, tone, and boundaries once and applies for the whole session or product - for instance "You're a support assistant for product X, always answer in German, and refer refunds to a human". The user prompt, by contrast, is the individual, changing request. Anyone not deliberately using this split ends up repeating the same base rules in every single request - error-prone and unnecessarily costly.
Forcing structured output instead of hoping for it
Instead of hoping the answer arrives in a certain text format, you can explicitly require a fixed schema (say, JSON with defined fields). That becomes essential once a downstream system is supposed to process the answer automatically - exactly the principle behind tools using MCP (see "What is MCP (Model Context Protocol)?"). Unstructured prose might be readable for a human, but it's a guessing game for a system behind it.
Few-shot examples instead of a long description
Showing two or three concrete example pairs (input → desired output) is often more reliable than describing the desired format in words. Instead of "write friendly and concise", two example responses in the desired tone show directly what's meant - the model transfers the pattern instead of interpreting an abstract instruction.
What belongs in the context - and what doesn't
More context isn't automatically better. "What is RAG (Retrieval-Augmented Generation)?" already shows the principle for company knowledge: only the few genuinely matching passages get included, not the whole database. Irrelevant context dilutes the model's attention and can actually make answer quality worse instead of better.
Why this matters for you as a decision-maker
In production, it pays to treat prompts like code: versioned, with clear test cases, instead of a one-off piece of text. "How to really check if an AI solution works" provides the matching test methodology. Deliberately using system prompts, structured output, few-shot examples, and context curation gets you noticeably more reliable results than any single, however well-intentioned, prompt.