Pin the Job Before the First Turn
A conversational prototype earns its keep when it proves one job, not five. Before you touch a prompt, write the single outcome the conversation should reach: a booked demo slot, a summarized support thread, a drafted onboarding script, or a generated release note. That sentence becomes the acceptance test for everything that follows.
List the non-goals next. If the prototype will not handle refunds, account changes, or long documents, say so in the brief and again in the system prompt. Scope leaks in chat are hard to notice because every turn looks harmless on its own, so the boundary needs to live in writing where a reviewer can see it.
Fix the shape of a turn while you are still on paper: who speaks first, how long a reply may run, what the user can attach, and what the assistant does when it is missing information. Conversational prototypes reward a strict turn grammar far more than they reward clever phrasing.
Decide where the prototype will run. A CLI session with Grok 4.7 is enough for the first lap, and it keeps the distance between idea and transcript short. Carry the same turn grammar into the eventual product so the prototype does not quietly invent a contract nobody intends to implement.
Write the System Prompt Like a Product Spec
Treat the system prompt as a spec with sections, not a paragraph of vibes. Open with the role and the voice, then the allowed actions, the refusal policy, the output format, and the fallback behavior for missing context. Each block should be short enough that you can change one without rewriting the others.
Separate policy from content. Policy covers tone, boundaries, and safety; content covers the domain facts the assistant needs for this particular job. When a product detail changes, you edit content. When a rule changes, you edit policy. Keeping them apart makes prompt review in a pull request far more useful than a single wall of prose.
Add two or three short golden turns instead of a dozen long examples. Show the opening move, a moment where the user is vague, and a moment where the assistant must ask a question instead of guessing. Example turns teach rhythm and restraint; oversized examples teach parroting and padding.
Version the prompt the way you version code. A dated note beside the prompt, a changelog entry, and a pointer to the eval run that justified the change turn prompt edits into decisions you can revisit later. Prototypes drift quickly, and an unversioned prompt is the first place that drift becomes invisible.
Design a Turn Loop That Survives Bad Input
Conversation state is where prototypes quietly break. Decide what you keep between turns: the last user goal, the tool results already used, the draft the user is editing. Everything else should be compressed into a short running brief rather than replayed verbatim, so context grows in a way you can reason about and debug.
Tool calls deserve the same discipline. Give each tool one job, a typed input, and an explicit error message the model can read aloud as a graceful reply. A prototype that surfaces raw stack traces in the chat has not failed gracefully; it has leaked its plumbing into the conversation.
- Keep a short running brief of the goal and the decisions so far, and refresh it at each turn instead of replaying the whole transcript.
- Give every tool a single typed input, one clear purpose, and an error message the assistant can turn into a calm, useful reply.
- Cap tool calls per turn and finish with a structured handoff object the host application can validate before it acts.
- Treat empty, duplicated, and contradictory user messages as first-class test cases, not as accidents that will never happen.
Guardrails belong in the loop, not in a wish. Decide in advance what the assistant does when a request falls outside scope, when a tool returns nothing, and when the user asks it to ignore earlier instructions. Write those responses once, test them, and let the model deliver them with the same care as the happy path.
Build an Eval Set You Can Run in One Lap
An eval set of twenty to fifty short threads will tell you more than a week of manual poking. Start with golden transcripts: the ideal conversation for the core job, one vague opener, one hostile turn, one turn that needs a tool, and one turn where the correct answer is a question. Keep each thread short enough to read at a glance.
Score with a simple rubric rather than a single pass or fail. Useful axes include goal reached, boundary respected, tone held, tool choice correct, and reply length appropriate to the turn. A small rubric gives you a signal you can act on when a prompt change helps one axis and hurts another.
Use a second model as a judge so your grading does not depend on the system under test. Claude Haiku 4.5 is a practical choice for rubric scoring on a prototype budget, and it is useful to keep one older turn sample from Grok 4.6 in the set as a reference transcript when you tune behavior. Human review still matters on the handful of threads where the rubric feels ambiguous.
Run the eval set on every prompt or tool change and record the result beside the version note. The point is not a leaderboard; it is a regression habit. Over a few laps you will see which sections of your system prompt actually move behavior, and which ones only make the prompt longer.
Wire the First Run Through the Relay
Once the conversation holds together, route it through a single endpoint instead of hard-coding model logic into the prototype. SwiftRelay is a pay-as-you-go relay for Claude, GPT, Gemini, DeepSeek, Kimi, GLM, Qwen, Grok, and other models, and it works with Claude Code, Gemini CLI, Codex, and any OpenAI-compatible SDK. Billing is per token, so a prototype only pays for the turns it actually runs.
The API host is https://api.fastapirelay.com. Claude Code and Gemini CLI take the bare host, while OpenAI SDKs add /v1 to the base URL. Getting this detail right is the difference between a green first run and a config detour.
A pay-as-you-go relay also makes your eval habit practical: you can run the whole set, inspect the transcripts, adjust the prompt, and run it again without negotiating anything. New accounts receive a small gift credit to cover those first iterations, and inviting friends earns 14% of their top-ups.
Before you call the lap complete, walk the checklist once more:
- The success sentence from the brief matches what the transcript actually achieves.
- The system prompt is versioned, split into policy and content, and reviewed like code.
- State, tool errors, and out-of-scope requests each have a defined behavior.
- The eval set runs end to end and the result is recorded beside the prompt version.
- The host, base path, and credentials are stored in configuration rather than in the prompt.
Sign up for SwiftRelay, claim the gift credit, and run your first lap with Grok 4.7 today. Then treat the prototype as the first version of a spec: every turn that surprised you becomes a new eval thread, and every eval thread becomes a reason to tighten the prompt before real users arrive.