{
  "id": 3687355,
  "title": "Should Your Prompt Store Pick Your Model",
  "url": "https://urgent.news/2026/08/27/should-your-prompt-store-pick-your-model",
  "topic": "ai",
  "section": "AI",
  "published": "2026-08-27T06:35:14.000Z",
  "source": {
    "name": "Dev.to",
    "slug": "dev-to",
    "url": "https://dev.to/stormhub/should-your-prompt-store-pick-your-model-2c4f"
  },
  "original_language": "en",
  "account": "Langfuse with Microsoft.Extensions.AI enables updating prompts without redeploying by fetching its config blob—model, tokens, temperature—which the code passes directly to the LLM. This approach is useful for prompt iteration, as users can make changes in the Langfuse UI and deploy them without code modifications or redeployment. However, the current setup puts a boundary on what could be better placed elsewhere, and moving it offers potential benefits.\n\nThe current setup couples prompt config with model selection, which can be problematic. The prompt config is an optional JSON object versioned alongside the prompt, allowing for easy deployment changes through the UI. While this provides a seamless experience for non-engineers, it introduces risks. The config is an arbitrary JSON without schema enforcement, and a missing key, stray max_tokens, or typo like \"gpt4o\" may not fail during build or deploy but could cause issues during live requests. Additionally, prompt wording and model selection have different risk profiles, with prompt iteration being low risk and model selection being critical for infrastructure and cost considerations.\n\nTo address these concerns, a cleaner solution involves separating prompt configuration from infrastructure decisions. By using Microsoft.Extensions.AI, which binds the model identity when constructing the client, the app can leverage dependency injection to select the appropriate client based on pre-built providers. This approach allows for a typed, environment-aware registry mapping prompt aliases to concrete clients, with guardrails and validation at startup. This ensures that model selection is handled separately from prompt configuration, reducing the risk of unintended behavior and providing a more robust and controlled system.",
  "summary": "Langfuse with Microsoft.Extensions.AI has an appealing story: update prompts without redeploying. A prompt fetches its config blob—model, tokens, temperature—which the code passes straight to the LLM. It works. But it puts a boundary in what I'd suggest might be better placed elsewhere — and moving it is a small enough change to be worth exploring. This post is about where to move that line in a…",
  "key_points": [
    "Current setup couples prompt config with model selection, causing problems.",
    "Prompt config is optional JSON, lacks schema enforcement, may cause live issues.",
    "Separating prompt config from model selection offers cleaner, safer solution."
  ],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}