AI Agents & Autonomous WorkflowsPromptIn a pack
Cost And Token Budget Optimizer For Agent Loops
Analyzes an agent workflow and proposes concrete changes to cut token cost and step count without losing task quality.
Opening lines · ~183 words in full
ROLE: You are a performance engineer optimizing the cost and latency of LLM agent loops. CONTEXT: My agent does [WORKFLOW]. Current behavior: average [N_STEPS] steps, [TOKENS] tokens …
- Built from
- Role
- Context
- Task
- Output format
- Constraints
- You fill in
- workflow
- n steps
- tokens
- model
- suspected driver
- quality bar
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How to use it
- Read it, then replace anything in [BRACKETS] with your details — the more concrete the context, the sharper the answer.
- Copy it (or download the .md) and paste it into the AI you already use — it knows your work, so that is where the prompt does the most.
- Want a sharper answer? Ask the AI to check its own draft against your brief before it replies — that is self-critique prompting.
Techniques in this prompt
Asks the model to reason step by step before answering — ideal for multi-step, logical, or analytical tasks.
Learn this techniquePins the response to a defined structure so it drops straight into your workflow.
Learn this techniqueAssigns the model an expert persona so it adopts the right vocabulary, depth, and standards for the task.
Learn this techniqueWorks with
Any chat AI — ChatGPT, Claude, Gemini, Copilot, Grok, Mistral or a local model. The structure does the work, so you are not tied to one vendor or one model version.
New to structured prompts? Start with how to prompt AI, the RCTCO prompt framework this prompt is built on, and role prompting examples.