Prompt engineering is the discipline of structuring instructions to a language model so that outputs are reliable, scoped, and on-task. In a production system, prompts are not chat-window improvisations — they are versioned artefacts tested against an evaluation harness on every change.
What prompt engineering includes.
- System prompts that define role, scope, voice, and the non-negotiable refusal rules for the agent.
- Few-shot examples picked from real fixtures, not invented personas, to anchor the model's pattern matching.
- Tool descriptions precise enough that the model picks the right tool without listing the wrong arguments.
- Output-shape contracts (JSON schema, function-call signature) the model is instructed to honour and the harness validates.
- Version metadata so every output traces back to the exact prompt + model + fixtures that produced it.
What prompt engineering is not.
It is not whispering magic phrases into a chat window. Not a one-off cleverness contest. Not a substitute for evals, retrieval, or safety rails. Prompts that look brilliant in a demo and degrade silently under production load are the single most common cause of failed AI launches.
“A prompt is code. Treat it like code.”
How Morvion treats prompts.
Every production prompt lives in version control next to the retrieval pipeline and the eval harness. Changes go through PR review the same way TypeScript does. Regression gates re-run the full fixture set on every prompt change and block the merge if any rubric metric regresses past tolerance. Prompt-level observability traces let us replay a failure end-to-end without re-running the model.
Frequently asked.
- What is prompt engineering?
- Prompt engineering is the discipline of structuring instructions to a language model so outputs are reliable, scoped, and on-task. In a production system, prompts are versioned artefacts tested against an evaluation harness on every change, not chat-window improvisations.
- Why version prompts in git?
- Because a prompt change is a code change. The output distribution shifts, the eval scores shift, the user-facing behaviour shifts. Treating prompts like code means PR review, regression gates, rollback, and traceability of every production output to the exact prompt that produced it.
- Is prompt engineering still important if you use RAG?
- Yes, more important. RAG controls what context the model sees; the prompt controls what it does with that context. A well-tuned retrieval pipeline can still produce bad outputs if the prompt allows the model to ignore the context or refuse incorrectly. Prompts and retrieval are co-designed.
- Does Morvion ship prompts as part of every AI engagement?
- Yes. Production prompts ship in version control, are run through the eval harness on every change, and carry version metadata in every output trace. Vibes-based prompt iteration does not ship.