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If your team changes a prompt more often than the surrounding software, treat that prompt as product content—not as a hard-coded string that must pass through code review and deployment for every edit. Maaz Kazi’s CASEY career-coaching example shows one way to do this: store session prompts and phase configuration in database tables, expose them through an admin surface, and keep decisions about workflow progression in deterministic backend logic.
Why prompt placement becomes a product decision
A prompt can shape what a user experiences: how a coaching session opens, what it tries to learn, and how it responds. When a team regularly adjusts that behavior, storing the prompt only in application code makes product iteration depend on engineering workflows, even when an edit is chiefly a change in wording or instructions.
Kazi’s test is practical: “if the thing you edit most often is a string in a prompt file, it is not code, it is content, and you are versioning it in the wrong place.” That is his design principle, not a universal engineering standard. A prompt that changes rarely, is tightly coupled to code, or requires engineering review for safety may still sensibly remain code-managed.
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Kazi describes CASEY as beginning as an instrument for testing whether spoken conversations could surface career-assessment constructs, rather than as a complete product build. Its coaching experience is organized into stages, with prompts and phase configuration stored in database tables and made available through an admin surface. In this arrangement, an authorized product owner can revise session behavior without deploying application code.
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The separation matters: editable configuration determines how a stage is conducted, while backend logic determines whether the user has met the conditions to move on. For example, the backend can require all three roadmap steps to be complete before advancing, regardless of how the conversation is phrased.
What journey does the coaching experience follow?
CASEY’s journey uses explicit phases rather than asking a freeform assistant to infer its place from the whole transcript.
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- Discovery: understand the user’s situation, interests, and blockers.
- Orientation: narrow toward a direction.
- Roadmap: work through three steps—explore, validate, and confirm.
- Accountability: return for recurring check-ins.
This structure gives the product a defined destination for each interaction. The model can help interpret what the user says, but application logic can decide what counts as completion and whether the next phase is unlocked.
What belongs in a prompt, and what belongs in backend logic?
A useful boundary is whether a change alters the experience’s expression or its rules. Kazi’s approach puts frequently tuned coaching behavior in prompt and configuration data, while keeping progression conditions in deterministic backend functions.
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- Prompt or configuration: tone, opening questions, phase instructions, and other behavior the product team expects to tune.
- Backend logic: phase transitions, completion criteria, and other rules that should behave predictably.
- Model assistance: extracting and summarizing signals from the conversation to inform the application.
This is not a claim that prompts are harmless or need no controls. If an edit could change safety boundaries, eligibility, or other consequential behavior, the product should define who can edit it and what review or testing is required before the change takes effect.
How does CASEY divide live conversation and follow-up work?
Kazi describes separate work for the live voice session and post-session processing. A voice-native conversational stack handles the real-time interaction, where latency affects the experience. After the call, a separate, less expensive text model can produce summaries and extract key insights without needing to respond in the moment.
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This is an architectural rationale, not a measured result: the source gives no benchmark figures or cost measurements. The split may be useful when some model tasks need real-time responses and others can wait, but the reported example does not establish that a two-model arrangement is best for every application.
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How to decide whether your prompts should leave code
Consider the change process, the owners, and the risks—not just where a prompt can technically be stored.
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- How often does behavior change? If prompt edits are frequent product iteration, a content/configuration workflow may be more appropriate than a code-only workflow.
- Who needs to make edits? Give access to the people responsible for the experience, while defining permissions and review appropriate to the consequences of a change.
- Does every edit need a deployment? Separating prompt data from code can remove deployment as a requirement for routine wording or phase-instruction updates.
- Which decisions must be predictable? Keep completion rules and transitions in backend logic when the product needs explicit, testable conditions.
- Which tasks must run in real time? Prioritize latency for live interaction; schedule summarization or extraction after the session when immediate output is unnecessary.
Moving prompts out of code is not automatically safer or faster. The source describes one practitioner’s choices and rationale, but provides no comparative performance data proving that database-managed prompts outperform code-managed prompts across products. Teams still need an appropriate editing and release process for the behavior they expose.
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