Product guide

LLM cache dashboard for prompt visibility and reusable answer control

PromptCacheAI does more than skip repeated model calls. The dashboard shows hit rates, repeated prompts, estimated savings, and the cached responses your app is reusing, so your team can improve answers over time.

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LLM cache dashboardprompt visibilityAI response observability

What the dashboard helps you understand

Capability

Question

Dashboard signal

Are repeated prompts hitting cache?

Look at total requests, hit rate, exact hits, and similarity hits.

Use the numbers to confirm the cache is creating value.

What are users asking repeatedly?

Search prompts and responses across cache entries.

Find recurring questions and high-value workflows.

Which namespaces are working?

Filter metrics and entries by namespace and date range.

Compare production, staging, tenant, and workflow caches.

Which answers need attention?

Open cached entries and inspect the prompt and response.

Edit the reusable response when an important answer should be improved.

See what users keep asking

Search prompt and response text to find repeated questions, recurring workflows, and prompts that are good candidates for reuse.

Filter by namespace and date range to understand how different apps, environments, or workflows behave over time.

Measure cache performance

• Track total requests and hit rate
• Separate exact hits from similarity hits
• View estimated savings from avoided model calls
• Filter entries by cached status and TTL status

Control reused answers

For high-value repeated prompts, you can inspect the full prompt and cached response. If an answer should be improved, edit the cached response so future cache hits return the updated version.

This gives teams a practical way to improve reusable answers without waiting for the next model call.

Best-fit workflows

• Support and FAQ assistants
• RAG apps with repeated document questions
• Internal copilots with stable knowledge requests
• QA, staging, demos, and evaluation loops

What not to use it for

PromptCacheAI is best for repeated prompts with reusable answers. For personalized prompts or live user data such as shipping status, billing status, account records, or private user details, use a live model or source-system call.

Related guides

LLM cache architecture

See how the dashboard fits into the broader cache workflow.

Reduce LLM costs

Connect cache analytics to avoided provider calls.

Prompt caching API

Implement the cache flow that feeds the dashboard.

What does the PromptCacheAI dashboard show?

The dashboard shows total requests, hit rate, exact hits, similarity hits, estimated savings, namespaces, TTL status, cached entries, prompts, and responses.

Can I search prompts and responses?

Yes. You can search prompt and response text, filter by namespace and date range, and focus the cache entries table by cached status or TTL status.

Can I edit cached responses?

Yes. You can inspect a cached response and update the answer PromptCacheAI should reuse for future cache hits.

Does PromptCacheAI replace product analytics?

No. PromptCacheAI is not a full product analytics platform. It gives cache-specific visibility into repeated prompts, reused answers, hit rates, and estimated savings.

Try PromptCacheAI in your stack

Launch a provider-agnostic prompt caching layer with namespaces, TTL controls, semantic matching, and usage visibility.

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