Why better prompts don't automatically use fewer credits
Reverse prompting, prompt optimisation, Cowork and a gelato use case that suddenly became rather expensive. What started as a session for the HELish Summit in Helsinki turned into a workload design experiment.
Research question
How do I design an AI workload so the model does exactly the work the use case actually requires?
Use Case Ticket
Why better prompts don't automatically use fewer credits
Scenario
Mid September 2026: Alpine Gelato has to end the summer, in theory. Mango, Raspberry and Lemon are still selling surprisingly well, while Cinnamon Plum and Chestnut need to go into production, the first Christmas orders are coming in and Vanilla is becoming more important for the Q4 business. Baked Apple has demand and ingredients, but no complete product release yet. The management question: which summer flavours should be phased out, which core flavours protected, which autumn and winter flavours ramped up?
Test setup · Inputs
v1- Inventory
- Sales Orders
- Seasonal Demand History
- Production Capacity
- Procurement & Lead Times
“A summary says what happened. A briefing shows what matters now.”
Workshop board
Hypothesis → Test → Observation
Research question
How do I design an AI workload so the model does exactly the work the use case actually requires?
Use Case Ticket
Why better prompts don't automatically use fewer credits
Scenario
Mid September 2026: Alpine Gelato has to end the summer, in theory. Mango, Raspberry and Lemon are still selling surprisingly well, while Cinnamon Plum and Chestnut need to go into production, the first Christmas orders are coming in and Vanilla is becoming more important for the Q4 business. Baked Apple has demand and ingredients, but no complete product release yet. The management question: which summer flavours should be phased out, which core flavours protected, which autumn and winter flavours ramped up?
Test setup · Inputs
Agent Blueprint · v1- Inventory
- Sales Orders
- Seasonal Demand History
- Production Capacity
- Procurement & Lead Times
“A summary says what happened. A briefing shows what matters now.”
Question Card
Starting question
Mid September 2026: Alpine Gelato has to end the summer, in theory. Mango, Raspberry and Lemon are still selling surprisingly well, while Cinnamon Plum and Chestnut need to go into production, the first Christmas orders are coming in and Vanilla is becoming more important for the Q4 business. Baked Apple has demand and ingredients, but no complete product release yet. The management question: which summer flavours should be phased out, which core flavours protected, which autumn and winter flavours ramped up?
Research question
How do I design an AI workload so the model does exactly the work the use case actually requires?
Setup Module
Test setup
Instead of writing an ever longer prompt, we started with reverse prompting. The SharePoint archive held planning documents from 2022 to 2025, from Season_Transition_Notes_2022.docx to FINAL_final_transition_notes.docx. A meta prompt was not meant to copy the output, but to reconstruct the method: objective, decision logic, sequence, business rules, quality checks. Out came rules such as: check Q4 demand before reducing core flavours; never assess inventory in isolation; review procurement risks before ramping up a flavour. In the second experiment the master prompt was optimised again, not shorter but more precise: clearer scope, explicit boundaries, stopping rules and a clear statement about which work the AI should explicitly not do.
- 01Inventory
- 02Sales Orders
- 03Seasonal Demand History
- 04Production Capacity
- 05Procurement & Lead Times
Prompt Box
The original prompts
Meta prompt 01
Reverse prompting: recover the recipe
Reconstructs the method behind existing work results and derives a reusable master prompt from it.
You are an experienced prompt engineer and work design analyst. Analyse the examples I give you as evidence of how this work functions when it is done well. Do not simply summarise them and do not copy them. Instead, reconstruct the underlying method. Identify: - the objective of the work - the recurring decision logic - information that materially influences the outcome - the sequence of work steps or considerations - recurring business rules and constraints - useful output structures - quality checks and decision points - assumptions that must be re-examined on every run - elements that are merely formatting, habit or historical noise Then create a reusable master prompt that lets an AI system perform the same kind of work with new inputs. The master prompt should: - preserve the useful decision logic without blindly adopting historical conclusions - clearly separate stable rules from assumptions that need to be re-examined - define the minimum necessary information - specify the expected output and decision categories - flag missing or contradictory information instead of inventing it - include explicit quality checks Before the final master prompt, first show me which logic you extracted and which elements you deliberately excluded, including your reasoning.
Meta prompt 02
Prompt optimisation: narrow the workload
Reviews an existing prompt for unnecessary work, unclear scope and missing boundaries, and delivers a more precise version.
You are an experienced prompt engineer and AI workload optimizer. Review the following prompt as if it were run repeatedly in an AI work environment where answer quality, unnecessary work and resource consumption matter. Optimise it regarding: - clarity of the actual objective - minimum necessary context - explicit scope and clear boundaries - precise use of sources and tools - redundant instructions - unnecessary research, analysis or output - clear decision criteria - handling of missing information and uncertainty - precise but complete output requirements Do not shorten the prompt simply for the sake of shortening it. Preserve everything that materially improves decision quality. First show: 1. What is unclear or unnecessarily broad 2. What could trigger unnecessary work 3. Which information is missing 4. What should be removed, merged or more clearly limited Then deliver: A. The optimised prompt B. What was changed and why C. Which work the AI should now deliberately no longer do D. Which remaining assumptions still require human judgement Prompt to optimise: [INSERT PROMPT]
Comparison Module
Results compared
Both runs use the same task and the same data. The difference is not the information, it is the instruction.
Broad vs. deliberately narrowed2,416 credits
Broad scope, no stopping rules: Cowork orchestrates more freely, researches more and produces longer output that nobody needs.
- —Scope too broad. The model keeps researching even though there is more than enough evidence already.
- —No stopping rule. No clear boundary for when the analysis ends.
- —Analysis-only test. 206 credits, exactly identical to Run B. Better prompts do not automatically save credits.
Same task, same data, same model, same effort level, only the instruction was different.
2,024 credits
Precise scope, explicit boundaries and clear rules about which work the AI should not do. Result: 392 credits less, 16.2 percent.
- 01
−392 credits (−16.2 %)
Not a benchmark, but one concrete workload: the narrowed instruction measurably needed less.
- 02
GPT-6 Astra: 1,717 credits
The same optimised workload with Astra: roughly 15 % below Run B and almost 29 % below Run A, and noticeably faster.
- 03
Vanilla: Protect / Increase
2,110 units in stock look like overstock, but Q4 demand, open orders and a long procurement lead time flip the recommendation.
- 04
Baked Apple: Delay / Investigate
Demand and ingredients are there, but the product release is not complete. Demand is not automatically a production release.
Model choice is part of workload design: the strongest model was not the most expensive one.
Why this matters