Software Development
LLM Prompt Optimizer
Analyzes prompt templates for token inefficiency, ambiguity, missing examples, and poor output specification, producing an optimized version with cost and quality deltas. Useful for teams running LLM-backed features in production. Engineers shipping LLM-backed features in production, founders evaluating prompt cost against runway, AI engineers reviewing prompts written by non-specialists. Teams ship prompts that work for the first 10 happy-path inputs and only learn about the inefficient structure, ambiguous instruction ordering, or unused examples after the monthly API bill arrives. Manual prompt optimization takes hours of trial and error. A structured optimizer surfaces the usual suspects — redundant system context, examples placed after instead of before instructions, output format not pinned, poor cacheability — in one pass, with cost deltas attached.
One-Time Purchase
$19.99
Prompt Optimizer — customer-support-triage (gpt-4o)
Target prompt: Production ticket classifier at FinTrack Platform. Routes inbound support tickets into one of five categories. ~500K calls/month.
Tokenizer: tiktoken cl100k_base
Baseline eval set: 500 human-labeled tickets, stratified across categories.
Headline
Summary
The original prompt carried about 30% dead weight — adjective stacking in the role block, prose category definitions, and example preambles that contributed no signal. The optimized version drops 253 tokens, preserves classification accuracy within margin, and improves malformed-JSON rate by pinning the output schema inline. Net monthly savings at current volume: roughly $380.
29.9% input-token reduction with accuracy held flat (within ±1pp on the 500-ticket eval set). Estimated savings at 500K calls/month: ~$380.
Before / After — Prompt Structure
Original prompt
847 tokens
Verbose, ambiguous output spec, poor cacheability
Optimized prompt
594 tokens
Tight role, inline schema, structured examples
Optimized Prompt (drop-in)
You are a customer support assistant for FinTrack. Be concise, empathetic, and solution-focused.
Classify the ticket into exactly one category.
Categories: Billing | Technical | Account | Integration | General
Respond in JSON:
{"category": "<value>", "confidence": 0.0-1.0, "suggested_action": "<string>"}
Input: My card was declined but the charge still posted.
Output: {"category": "Billing", "confidence": 0.92, "suggested_action": "Refund pending charge; confirm card on file"}
Input: The Stripe webhook stopped firing after last night's deploy.
Output: {"category": "Integration", "confidence": 0.94, "suggested_action": "Surface webhook delivery logs; engage integrations on-call"}
Input: I can't log in even after a password reset.
Output: {"category": "Account", "confidence": 0.88, "suggested_action": "Force session reset; verify SSO claims"}
Ticket: {{TICKET_BODY}}
Eval Results — 500-ticket Holdout
| Metric | Original | Optimized | Delta | Verdict |
|---|---|---|---|---|
| Overall accuracy | 91.4% | 91.0% | −0.4pp | Within margin |
| Macro-F1 | 0.89 | 0.89 | 0.00 | Flat |
| Malformed JSON rate | 3.2% | 0.6% | −2.6pp | Improved |
| Avg input cost / call | $0.00254 | $0.00178 | −$0.00076 | −29.9% |
| p50 latency | 612ms | 538ms | −74ms | Modest |
| Edge-case precision (Billing vs Shipping) | 0.84 | 0.79 | −5pp | Watch |
Per-Change Log
Change 1 — Role block condensation Neutral Adjective stack ("helpful, friendly, knowledgeable") collapsed into a single directive. 41 → 18 tokens. No semantic loss.
Change 2 — Task instruction simplification Neutral "Do not choose more than one" is implied by "exactly one." 52 → 9 tokens.
Change 3 — Category list reformatting Watch edge cases Inline prose definitions removed. 73 → 11 tokens. Risk of cross-category confusion on ambiguous tickets (e.g., refund-on-shipped-item).
Change 4 — Output format spec Positive Schema pinned inline. Malformed-JSON rate dropped from 3.2% to 0.6% on the eval set.
Change 5 — Few-shot preambles removed Neutral LLMs use the input/output pairs; preamble adds no signal.
Change 6 — Closing filler removed Neutral Behavior already established in role block.
Cache-Hit Analysis
Below cache threshold
At 594 tokens the optimized prompt sits below OpenAI's 1,024-token prefix-cache threshold, so prompt caching is not applicable at current size. If a product knowledge block is added later, place it after the role + categories + schema so the static prefix grows past 1,024 tokens. Estimated cacheable prefix at that point: ~610 tokens, reducing effective input cost by ~25% on cache hits.
Recommendations
Ship the optimized prompt
Accuracy is within margin, JSON validity is meaningfully better, and the cost delta is real. Roll out behind a feature flag and watch edge-case precision for two weeks before removing the flag.
Edge-case regression to monitor
Billing-vs-Shipping precision dropped 5pp on the eval set. If production tickets carry a higher share of refund-on-shipped-item cases than the eval distribution, restore a one-line disambiguation hint ("refunds on delivered orders are Billing"). Cost of that hint: ~14 tokens. Worth it if the regression is real.
Rollback trigger
Revert if overall accuracy drops more than 1pp below original on the next 1,000 production tickets, OR if any single category's precision falls below 0.80. Keep the original prompt available under flag triage_prompt_v1 for the first 30 days.
This sample illustrates the skill's output format. FinTrack Platform is a fictional company used recurringly across these sample outputs. Real production prompts and ticket data are never included in sample outputs.
View full sample →
All sales final. No refunds on digital products.
Includes support for Claude Code, Codex, OpenClaw, and Google Antigravity in the same license.
Also in AI Engineering
Bundle price: $55. Compare this skill with the full workflow bundle or Pro access.
Best for
Engineers running LLM-backed features in production where the monthly API bill is large enough to be worth optimizing — typically when token spend crosses a few thousand dollars per month. Especially valuable for AI engineering leads reviewing prompts written by non-specialists and wanting to surface inefficient structure, ambiguity, or poor cacheability in a single pass.
Not ideal for
Prompts still in heavy iteration where the output behavior is what’s being tuned and structural optimization is premature. Also a poor fit when the cost driver is volume rather than prompt shape — sometimes the right answer is a smaller model or a cache, not a sharper prompt.
Included in this purchase
- Claude Code, Codex, OpenClaw, and Google Antigravity skill files.
- Setup guidance for the right adapter in your workspace.
- One-time license for the purchased skill version.
Setup
Plan for a short setup in the repository or workspace where the skill will run. Some coding familiarity helps for implementation-heavy outputs.
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Future Updates
This purchase includes the current version of the skill. If you want future adapter updates — meaning compatibility and packaging updates as supported platforms evolve — plus new catalog additions included automatically, upgrade to Pro.