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fix(deployment-strategies): Remove duplicated information from run approach #218

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fix(deployment-strategies): Remove duplicated information from run approach #218

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14 changes: 4 additions & 10 deletions onboarding-guides/deployment-strategies.mdx
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Expand Up @@ -20,9 +20,6 @@ PromptLayer fits into your stack at three levels of sophistication:
# Use `promptlayer_client.run` (quickest path)

When every millisecond of developer time counts, call `promptlayer_client.run()` directly from your application code.
1. Fetch latest prompt – We pull the template (by version or release label) from PromptLayer.
2. Execute – The SDK sends the populated prompt to OpenAI, Anthropic, Gemini, etc.
3. Log – The raw request/response pair is saved back to PromptLayer.

<CodeGroup>

Expand Down Expand Up @@ -52,12 +49,9 @@ const response = await plClient.run({

**Under the hood**

1. SDK pulls the latest prompt (or the version/label you specify).
2. Your client calls the model provider (OpenAI, Anthropic, Gemini, …).
3. SDK writes the log back to PromptLayer.

> 💡 **Tip** – If latency is critical, enqueue the log to a background worker and let your request return immediately.
---
1. Fetch latest prompt – We pull the template (by version or release label) from PromptLayer.
2. Execute – The SDK sends the populated prompt to OpenAI, Anthropic, Gemini, etc.
3. Log – The raw request/response pair is saved back to PromptLayer.---

# Cache prompts with Webhooks

Expand Down Expand Up @@ -105,7 +99,7 @@ llm_response = openai.chat.completions.create(...)
queue.enqueue(track_to_promptlayer, llm_response)
```

> **Tip:** Most teams push the track_to_promptlayer onto a Redis or SQS queue so as to not block on the logging of a request.
> 💡 **Tip:** Most teams push the track_to_promptlayer onto a Redis or SQS queue so as to not block on the logging of a request.

Read the full guide: **[PromptLayer Webhooks ↗](/features/prompt-registry/webhooks)**

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