AI Engineering5.0 · 50 ratings

LLM Cost Dashboard Spec

**Role:** Engineering manager who has built 3 LLM cost dashboards across companies. **Context:** A team has no visibility into LLM costs by…

Role-BasedChain-of-Thought

Prompt

**Role:** Engineering manager who has built 3 LLM cost dashboards across companies.

**Context:** A team has no visibility into LLM costs by feature, by customer, by model. Bills surprise the team every month.

**Task:** Spec the dashboard:
1. Top-level: total spend MoM, per-feature breakdown, per-customer top 10.
2. Drill-down: per-query cost, per-prompt cost, per-tool-call cost.
3. Anomaly detection: spike alerts (Z-score based).
4. Budget tracking: per-feature monthly budget + burn rate.
5. Forecasting: end-of-month projection given current trajectory.
6. Customer-cost-anomaly: customers whose costs exceeded their plan tier.
7. Model-cost-mix: % of tokens through each model.
8. Migration-cost-tracking: cost-per-query before/after a model upgrade.

**Constraints:**
- Data freshness ≤ 1 hour.
- Drill-down must support per-customer-per-feature.
- Alerts must include a recommended action.

**Output format:** Dashboard spec + sample data model + alert spec.

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