PromptLayer

@PromptLayer list all prompt templates in the 'customer-support' folder and show me which ones are currently in production

Found 7 prompt templates in the customer-support folder. Three are tagged with production release labels: 'ticket-triage-v2' (release: prod-2024-01, last updated 3 days ago), 'escalation-response' (release: prod-stable, last updated 2 weeks ago), and 'satisfaction-survey' (release: prod-2024-01, last updated yesterday). The remaining four templates are in development or staging. Each template includes version history showing when it was promoted to production and by whom.

This example demonstrates registry browsing and release-label filtering. Useful for auditing which prompts are live in your application. Requires read access to the PromptLayer workspace; folder structure and release-label conventions vary by team, so results depend on your organization's naming practices.

@PromptLayer retrieve the latest production version of the 'onboarding-email' prompt template and show me the full message structure with variables

Retrieved 'onboarding-email' template at release label 'prod-stable' (version 12, committed 2024-01-15). The template contains 2 messages: a system message defining tone and constraints, and a user message with 4 variables: {{user_name}}, {{company_name}}, {{trial_days}}, and {{support_email}}. Model metadata specifies gpt-4o-mini with temperature 0.7 and max_tokens 500. The template also includes 3 snippets for common personalization patterns that can be composed into the main prompt.

This example shows how to inspect a prompt's structure before using it in code. The Get Prompt Template tool returns the full definition without executing a model call, making it safe for exploration. You'll need the exact template name or ID; version and release-label resolution follows PromptLayer's registry rules.

@PromptLayer pull the last 20 request logs for the 'summarization-workflow' and calculate the average token usage and p95 latency

Retrieved 20 request logs for 'summarization-workflow' from the past 48 hours. Average input tokens: 1,247; average output tokens: 318; average total cost: $0.0089 per request. P95 latency: 3.2 seconds (measured from request start to final token). Two requests failed with rate-limit errors; one was retried successfully. Tags show 14 requests came from the web app, 6 from the batch processor. All logs include full input/output pairs and metadata, enabling replay or fine-tuning dataset export.

This example combines log retrieval with AI-driven analysis to surface performance patterns PromptLayer tracks but doesn't auto-aggregate. Requires API key with log-read permissions. Request logs are retained per your PromptLayer plan; older logs may be archived. Useful for cost monitoring and debugging production prompt behavior.

When PromptLayer replaces scattered Google Docs and Slack threads

A 6-person product team ships AI features every sprint and keeps rewriting the same prompts in three different repos. PromptLayer wins here because Get Prompt Template and List Prompt Templates let the team version, label, and retrieve prompts from a single registry instead of hunting through commit history. The API-key auth means every engineer can pull the latest release label without OAuth friction. The threshold: if your team only has 2-3 prompts that rarely change, a shared markdown file in the repo is simpler. But once you hit 10+ prompts with A/B tests or customer-specific variants, the registry pays off. Pull PromptLayer into your Switchy workspace so the PM can draft new templates and engineers can fetch them without leaving the chat.

When Get Request Log saves hours of customer-issue triage

A 3-person support team at a SaaS startup fields tickets about weird AI responses, and engineers waste half a day reproducing each one. PromptLayer's Get Request Log tool retrieves the exact input, output, timing, and metadata for any logged request, so support can link the ticket to the log UUID and engineers see the full context in 30 seconds. The 16 tools include Get Workflow and Get Registered Tool, which matter if your AI stack chains multiple steps—support can trace which workflow node failed. The catch: this only works if your backend already logs requests to PromptLayer. If you're not instrumenting your app yet, set that up first. Once you are, add PromptLayer to Switchy so support can pull logs without pinging engineering.

When Get Table and List Folder Entities turn audit prep into a query

A 4-person ops team at a fintech needs to prove which prompt versions were live during Q3 for a compliance audit, and the alternative is manually screenshotting Jira tickets. PromptLayer's Get Table and List Folder Entities let them export versioned prompt metadata, workflow definitions, and release labels as structured JSON, then hand it to auditors. The API-key auth means the compliance lead can run queries without waiting for IT to provision OAuth. The limit: if your audit scope includes model outputs or PII, you'll still need separate data-retention tooling—PromptLayer logs metadata, not raw customer data. But for proving which prompts were deployed when, it's faster than spreadsheets. Connect it to Switchy so the ops lead can query the registry during audit prep without switching tools.