Same output quality. Lower token cost.
Your LLM Outputs Are Inconsistent and Your API Bill Keeps Climbing.
Monthly prompt refinement, A/B testing, parameter tuning, context window management, and cost reduction for every LLM-powered system you run. Output quality up. API spend down.
Prompt Engineering & LLM Optimisation is a monthly retainer service offered by Crescent AI, an AI automation agency helping small and medium businesses automate repetitive workflows without hiring in-house AI engineers.
MONTHLY
Prompt A/B testing across production LLM systems
20-40%
Typical cost reduction from context optimisation alone
TRACKED
Every prompt change measured before and after
School uniform season and wedding alterations back-to-back — customers ringing all day asking if their order was ready. We had jobs written on paper and no way to track any of it. Crescent AI set up automated SMS updates at every stage — dropped off, in progress, ready to collect. Calls have nearly stopped, I'm taking on twice the jobs, and it's still just the two of us.
Owner, Tailoring Business · Adelaide, Australia
The Problem
A bad prompt costs the same as a good one. It just produces worse results.
Most LLM systems go live with the prompts that passed testing. They're rarely revisited. As the use case matures, edge cases accumulate, costs rise, and outputs drift toward inconsistency. Prompt engineering isn't something you do once — it's continuous work that compounds: better outputs, lower cost, fewer downstream errors.
LLM outputs vary in quality depending on input phrasing — no structured prompt to standardise results
API costs rise as usage scales, with no analysis of what's driving token consumption
Context windows fill with irrelevant content that costs tokens without improving outputs
Prompt changes are made ad hoc — no testing, no version control, no rollback when something breaks
How it works
How the Retainer Runs
Baseline Prompt Audit
Month 1Every production prompt reviewed against your defined quality criteria — accuracy, tone, format compliance, task completion.
Token & Cost Analysis
Month 1Context window usage, prompt length, and system instructions analysed to find where tokens are being spent without improving output.
First Optimisation Pass
Month 1Redundant tokens removed, parameters reviewed, and the first round of A/B-tested prompt variants deployed against the baseline.
Monthly A/B Testing
OngoingNew prompt variants tested against your quality criteria each month. Changes deployed only when the data supports them.
Context & Parameter Tuning
OngoingRetrieval strategy, chunking logic, temperature, and model selection reviewed against your use case.
Prompt Version Control & Reporting
OngoingEvery change tracked with before/after quality metrics, full rollback capability, and a monthly summary of what shifted and why.
What's included
What the Retainer Covers
Prompt A/B Testing
Structured testing of prompt variants against your defined quality criteria. Changes deployed only when the data supports them — not based on intuition.
Token Optimisation
Analysis of context window usage, prompt length, and system instruction structure. Redundant tokens removed. Cost per output tracked and reduced.
Parameter Tuning
Temperature, top-p, frequency penalty, and model selection reviewed against your use case monthly. Default settings are rarely optimal settings.
Context Window Management
Retrieval strategy, chunking logic, and context injection reviewed and improved. Better context — more relevant outputs without longer (and more expensive) prompts.
Prompt Version Control
All prompt changes tracked with before/after quality metrics. Full rollback capability. History of what changed, why, and what it produced.
Coverage
LLM Providers We Optimise For
Works with your stack
Fit check
This is built for you if…
This service delivers the most value when:
Product and engineering teams running LLM features whose output quality is inconsistent
Operations teams whose AI API spend is rising without a clear explanation
Businesses using off-the-shelf AI tools where prompt control is possible but not being exercised
Anyone who shipped an LLM system and hasn't revisited the prompts since launch
Why Crescent AI
Why Choose Crescent AI for Prompt Optimisation
Where the risk sits
Month-to-month. Every optimisation is documented with before/after data. If costs aren't tracking down after 90 days, we'll show you exactly why — and you can cancel.
Start your retainer
Start with the Audit. Not a Sales Call.
30 minutes. We map the workflows eating your team's time, rank your top automations by ROI, and tell you honestly what's not worth touching yet. You get a written summary. No slide deck. No pitch.
Month-to-month. Every change measured before and after. Cancel any time.