What is the difference between Prompt Optimizer and a prompt library?

Unlike libraries that just store text, Prompt Optimizer uses a 3-tier AI pipeline to actively rewrite, evaluate, and route prompts. It provides deterministic assertions to ensure your prompts don't break in production.

Does Prompt Optimizer reduce API costs?

Cost impact depends on your prompt mix, not a fixed percentage. The 3-tier router sends simple, unambiguous requests through deterministic rules with zero LLM call — in production that's roughly a quarter of traffic. Duplicate requests are cached at zero cost. The optimization layer itself adds structure to your prompt, which typically increases token count rather than reducing it, except for already-verbose prompts or long chat histories, where it can trim redundant context. Prompt Optimizer's value is prompt quality and reliability, not guaranteed token savings.

Can I use Prompt Optimizer with Claude Desktop or Cursor?

Yes, Prompt Optimizer is MCP-native. You can connect it directly to Claude Desktop, Cursor, or any MCP-compatible IDE to optimize prompts where you actually code.

ContextOS — MCP-native infrastructure

Enforce production guardrails on every AI prompt

Try it free — no signup

Free to start No credit card

RouterZero-cost, 3 tiers

Evaluation10 assertion types

DeliveryRuntime API, no redeploy

RepairClosed-loop, automatic

The router decides before you pay

Three tiers. Simple requests resolve on deterministic rules at zero cost; only genuine reasoning reaches a frontier model.

Router — live

"Format this JSON response…"

Rules-based layer
Regex match, resolved locally

0ms · $0

"Analyze our competitive position in Q1…"

Frontier LLM (GPT-4o)
No match — escalated for reasoning

~800ms · $0.03

"Always prioritize safety above clarity…"

Value hierarchy
Non-negotiable — L2 floor applied

LLM_BASED enforced

Cost impact depends on your prompt mix, not a fixed percentage. Duplicate requests are cached at zero cost.

Prompts stop shipping with deploys

Fetch the compiled prompt at runtime. Update once; live everywhere, no redeploy.

# Fetch compiled prompt at runtime

curl https://api.promptoptimizer.xyz/api/v1/prompts/support-bot-abc12345/compiled \

-H "Authorization: Bearer sk-opt-yourkey" \

-d '{"variables": {"user_name": "Alex", "plan": "Pro"}}'

Runtime fetch

Always serve the latest optimized version — no redeploy needed.

{{variable}} interpolation

Inject user-specific values at serve time. One template, infinite variations.

Webhook on change

HMAC-signed webhooks notify your system the moment a prompt is updated.

Proof, not vibes

Deterministic assertions, constraint checks, semantic drift — then a repair loop that runs itself.

Assertion types

Length limits, required phrases, JSON validity, regex, latency — plus LLM-rubric scores. A measurable pass rate, not a gut feeling.

100%

Constraints checked

Word limits, formats, tone directives and persona rules are verified explicitly after optimization. Drop one and the check fails.

0–1

Semantic drift

Cosine similarity between original and optimized embeddings flags when "improvement" changed what you were asking for.

Live repair event stream

✓Optimization completescore: 0.58

⚠REPAIR_QUEUED — below threshold (0.65)

↻Repair iteration 1 running…

✓Repair completescore: 0.74 ↑

Output delivered. No manual retry needed.

Automatic quality scoring

Every optimization is scored 0–1.0 for quality, context preservation and semantic accuracy. You see the score, not a black box.

Repair loop on low scores

Below 0.65 the system queues a repair iteration and keeps refining — or tells you why it could not.

Real-time streaming

Repair events arrive over WebSocket: when it starts, what improved, when it is done.

Four ways to run it

Cloud for speed. MCP Server for control. Intent Engineering for precision — ranked goals that shape routing and LLM behavior simultaneously.

Cloud

Cloud Edition

Zero-setup, always up to date. Works across web, extensions, and MCP clients.

Self-host

MCP Server

Deploy Prompt Optimizer on your infra with enterprise controls and auditing.

Guardrails

Quality Guardrails

Define a set of non-negotiable rules for your AI. Automatically prevent hallucinations by enforcing strict response formats and quality standards across all your prompts.

Ranked quality rules

Injection + hallucination blocks

Cost-aware auto-routing

See glossary →

Governance