For business
What it is
Assembling exactly what the model sees: instructions, the retrieved sources, client facts and relevant history, in the right order and size.
Why it matters
Answer quality depends more on what goes into the prompt than on which model you pay for. Done well, it is also the biggest cost lever.
Risk without it
Overstuffed prompts are slower, pricier and less accurate; missing context produces generic answers.
For engineers
How it works
A template puts stable parts first (role, rules, output format) so they can be prompt-cached, then the variable parts: sources with ids, CRM facts and short memory. A token budget per section decides what is trimmed or summarised.
Typical stack
- prompt templates in code
- tokenisers
- provider prompt caching
- memory stores
Pitfalls & trade-offs
- Keep cacheable content first and byte-identical.
- Label sources with ids so the model can cite them.
- Version prompts like code and test changes against evals.
In this example
In
→ rules + 5 chunks + CRM + memory
Out
one prompt within token budget prefix: cached
Fictional sample · not a client system