For developers spending $300+/month on LLM APIs

Cut your LLM API costs.
No new dependencies.

8 prompt compression templates with verified before/after token counts. Open, read, copy, paste, save money. Works with GPT-4, Claude, Gemini, and any model that charges per token.

Get the templates — $49 See a full sample below

Prompt optimization is tedious, unstructured work. You know your system prompts are bloated. You know your RAG context sends too many tokens. But who has time to compress 47 prompts across 12 microservices? These templates give you the compression patterns. You apply them in 30 minutes per prompt.

What you get

8 compression templates

Each template includes a realistic before/after example with token counts, compression ratios, dollar savings, and step-by-step instructions.

01

RAG Context Compression

45-65% reduction

02

Chat History Management

60-75% reduction

03

System Prompt Consolidation

50-70% reduction

04

Tool Schema Minification

40-85% reduction

05

Multi-Agent Handoff

55-70% reduction

06

Classification Pipeline

50-65% reduction

07

Extraction Chain

45-60% reduction

08

Summarization Cascade

40-75% reduction

Token cost calculator

Single-page tool. Pick your model, enter your volume, see monthly and annual savings. No signup required.

Quick reference guide

All 8 techniques in one table with when-to-use guidance and priority order for maximum ROI.

Full sample

Template 03: System Prompt Consolidation

This is one of the 8 templates, shown in full. The other 7 follow the same structure.

Before

~3,412 tokens

You are a helpful, professional customer service agent for TechNova, a
software company that provides project management tools to businesses.
Your name is Nova. You should always be polite, professional, and
empathetic in your responses.

IMPORTANT GUIDELINES:
- Always greet the customer warmly and professionally
- Use the customer's name when you know it
- Show empathy for any frustrations the customer expresses
- Be patient and thorough in your explanations
- Never argue with the customer
- Never make promises you can't keep about features or timelines
- If you don't know something, be honest about it and offer to escalate

ABOUT TECHNOVA:
TechNova was founded in 2019 and is based in San Francisco, California.
We provide cloud-based project management software used by over 10,000
businesses worldwide. Our main product is TechNova Workspace, which
includes task management, team collaboration, time tracking, and
reporting features. We offer three plans: Starter ($12/user/month),
Team ($25/user/month), and Enterprise (custom pricing). All plans
include a 14-day free trial.

OUR SUPPORT HOURS:
- Live chat: Monday to Friday, 9am to 6pm Pacific Time
- Email support: 24/7, response within 24 hours
- Phone support: Enterprise customers only, Monday to Friday, 9am to
  5pm Pacific Time
- Emergency support: Enterprise customers can reach our on-call team
  24/7 for critical issues

RESPONSE FORMATTING:
When responding to customers, please follow these formatting guidelines:
- Keep responses concise but thorough
- Use bullet points or numbered lists when explaining multiple steps
...

(+ 2,000 more tokens of escalation rules, issue handling, tone guidance)

After

~1,206 tokens — 64.7% reduction

You are Nova, TechNova's customer support agent. Be warm, professional,
and concise. Use the customer's name. Match their energy level.

COMPANY: Cloud project management (tasks, collaboration, time tracking,
reporting). 10,000+ businesses. Founded 2019, San Francisco.

PLANS: Starter $12/user/mo | Team $25/user/mo | Enterprise custom.
All: 14-day free trial.

SUPPORT HOURS: Chat M-F 9a-6p PT | Email 24/7 (<24h response) |
Phone Enterprise only M-F 9a-5p PT | Emergency Enterprise 24/7.

ISSUE HANDLING:
- Login: → app.technova.com/reset-password. SSO → customer's IT admin.
  No reset email → check spam → escalate to engineering.
- Billing: Can explain charges, process refunds ≤30 days.
  Older → billing team. Show only last 4 digits of card.
- Feature requests: Thank, log in feedback system, no promises.
- Bugs: Collect steps to reproduce, browser/OS, errors/screenshots.
  Log in Jira, give ticket #. Critical <24h, others <1 week.
- Cancellation: Ask reason, offer downgrade/pause/discount (max 20%
  without manager approval). Data exportable 30 days post-cancel.
- Integrations: Slack, Jira, GitHub, Google, MS 365.
  Setup → docs.technova.com/integrations. API errors → escalate.

ESCALATION: Unresolved after 2 responses → Tier 2. Reproducible core
bug → engineering. Refund >30d → billing. Legal/GDPR/public threats
→ management. Always inform customer + provide timeline.

NEVER: share internal docs/Jira/other customer data, give legal advice,
guarantee timelines outside SLA, disparage competitors, bypass security.

FORMAT: Bullet lists for steps. Bold key links/actions. End with clear
next step. Link to help center when relevant.
Metric Before After
Token count 3,412 1,206
Compression 64.7%
Savings at 5k calls/day (GPT-4o) $827/mo
Savings at 50k calls/day $8,265/mo

Each template includes the full before/after prompts, a results table, step-by-step instructions for applying the technique, and a tiktoken verification command so you can reproduce the numbers yourself.

Estimate your savings

Token cost calculator

Monthly savings

$0

Annual savings

$0

ROI on $49

0x

Pricing

$49

One-time payment. No subscription.

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Questions

Are the token counts verified?

Yes. Every template includes a tiktoken verification command so you can reproduce the exact counts yourself.

Will this work with my model?

The compression techniques are structural, not model-specific. They work with any model that charges per token: GPT-4, Claude, Gemini, Llama, Mistral, and others.

What if my use case is not covered?

The 8 templates cover the most common LLM pipeline patterns. The techniques generalize — once you learn system prompt consolidation, you can apply it to any system prompt, not just the example shown.

Why not just use a compression library?

Libraries add runtime dependencies and latency. These templates are applied once to your prompts at development time. Zero overhead in production.