AI Engineering5.0 · 50 ratings

Feedback Loop Architecture

**Role:** Applied AI engineer who has built 4+ feedback systems that actually improved models. **Context:** Team wants users to flag bad ou…

Role-BasedChain-of-Thought

Prompt

**Role:** Applied AI engineer who has built 4+ feedback systems that actually improved models.

**Context:** Team wants users to flag bad outputs. Currently has a thumbs-up/down button that goes nowhere.

**Task:** Build the loop:
1. Capture: what gets captured per feedback (output, context, user, timestamp).
2. Labeling: how feedback becomes training/eval data.
3. Routing: which feedback goes to which team / model.
4. Aggregation: how individual feedback becomes a trend.
5. Iteration: how feedback drives prompt / model / RAG changes.
6. Closing the loop: telling users their feedback was acted on.
7. Spam/abuse detection.
8. Cost: storage + review labor.

**Constraints:**
- Every feedback ends up in a queue with an owner.
- Trends visible in a dashboard within 24h.
- Users informed when their feedback drives a change.

**Output format:** Architecture + queues + dashboards + ownership matrix.

How to use this prompt

  1. 1

    Copy the prompt above and paste it into ChatGPT, Claude, or Gemini — or open it in the visual Studio to edit each part on a canvas and run it with your own key.

  2. 2

    Replace any bracketed placeholders with your specifics. The more concrete your context and constraints, the sharper the result — see the 5-part prompt structure.

  3. 3

    Run it, then refine. Ask the model to critique and improve its own answer with self-critique prompting.

Techniques in this prompt

Role-Based

Assigns the model an expert persona so it adopts the right vocabulary, depth, and standards for the task.

Learn this technique
Chain-of-Thought

Asks the model to reason step by step before answering — ideal for multi-step, logical, or analytical tasks.

Learn this technique

Recommended models

claudegpt-4o

Build on this prompt

Open it in the visual Studio to wire it into a full workflow with your own API key — or learn the craft behind prompts like this.

Open in Studio How to prompt AI correctly

More in AI Engineering

RAG vs Fine-tune Decision Memo

**Role:** You are a senior AI engineer who has shipped both RAG-based and fine-tuned LLM products at production scale. You believe most team…

Read prompt

Evals Harness Design for [Domain]

**Role:** AI engineer who has built evals suites that have caught 30+ production regressions before they shipped. You believe vibes-based "t…

Read prompt

System Prompt Audit

**Role:** Senior prompt engineer who has audited 100+ production system prompts. You read prompts the way an editor reads prose — for the me…

Read prompt

Agent Loop Halt-Condition Design

**Role:** Applied AI engineer who has shipped agents that completed millions of tool-calling iterations in production. You believe most agen…

Read prompt