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docs(release-notes): update v1.81.14 - split guardrail sections, add eval results, fix key highlights and section placement (#21847)

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docs/my-website/release_notes/v1.81.14.md
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@ -42,10 +42,9 @@ pip install litellm==1.81.14
## Key Highlights
- **Guardrail Model Garden** - [Browse and deploy production-ready guardrail policies in one click — competitor blockers, topic blockers, keyword filters, GDPR PII, EU AI Act, prompt injection, and more](../../docs/proxy/guardrails/policy_templates)
- **Block topics, competitors, and keywords at the gateway — no external API** - [3 new built-in guardrails: competitor name blocker, topic blocker, and insults/keyword filter. Define your own lists, apply to specific teams or keys, swap providers without changing code](../../docs/proxy/guardrails)
- **See what your guardrails are actually doing in production** - [Guardrail tracing UI shows policy hits, detection method, and match details per request — know exactly when and why a guardrail fired](../../docs/proxy/guardrails)
- **Test guardrail policies before you ship them** - [Upload a CSV dataset to the compliance playground and validate against real traffic. Get AI-generated policy suggestions with latency estimates before anything goes live](../../docs/proxy/guardrails/policy_templates)
- **Guardrail Model Garden** - [Easily browse built-in and partner guardrails by use case — competitor blockers, topic filters, keyword lists, GDPR PII, EU AI Act, prompt injection, and more](../../docs/proxy/guardrails/policy_templates)
- **3 new built-in guardrails** - [Competitor name blocker, topic blocker, and insults/keyword filter — all run at the gateway level, no external API, configurable per-team or key](../../docs/proxy/guardrails)
- **Compliance Playground** - [Run your guardrails against your own datasets or pre-built ones to measure performance before going live](../../docs/proxy/guardrails/policy_templates)
- **Claude Sonnet 4.6 — day 0** - [Full support across Anthropic and Vertex AI: reasoning, computer use, prompt caching, 200K context](../../docs/providers/anthropic)
- **Major performance batch** - 20+ targeted optimizations across router algorithms, logging overhead, cost calculator, and connection management — meaningfully lower latency and CPU overhead on every request
@ -53,11 +52,15 @@ pip install litellm==1.81.14
## Guardrail Model Garden
This release ships a library of pre-built guardrail policies you can deploy in one click — competitor blockers, topic filters, keyword lists, GDPR/EU AI Act compliance templates, and prompt injection detection. Pick a template, customize the parameters (keyword lists, blocked topics, score thresholds), and attach it to a team or key. No need to write guardrail logic from scratch.
Browse built-in and partner guardrails organized by use case — competitor blocking, topic filtering, keyword lists, GDPR/EU AI Act compliance, prompt injection detection, and more. Pick a template, customize the parameters (keyword lists, blocked topics, score thresholds), and attach it to a team or key.
Three new built-in guardrails ship alongside the Garden: a competitor name blocker, a topic blocker (keyword and embedding-based), and an insults/keyword filter. All run at the gateway level with no external API call. They're configurable per-team or per-key, and you can swap in AWS Bedrock Guardrails or Azure Content Safety on the same endpoint without changing your application code.
## 3 New Built-in Guardrails
Guardrail tracing is also included: every request that triggers a guardrail now logs the policy name, detection method, and exact match in the logs view. When a policy fires too often in production, you can see exactly which rule triggered and on what input.
A competitor name blocker, a topic blocker (keyword and embedding-based), and an insults/keyword filter. All run at the gateway level with no external API call. They're configurable per-team or per-key, and you can swap in AWS Bedrock Guardrails or Azure Content Safety on the same endpoint without changing your application code.
## Compliance Playground
Upload your own dataset or use a pre-built one to measure how a guardrail policy performs before it goes live. See precision, recall, and false positive rate on your actual traffic patterns — so you know how the policy will behave in production before you deploy it.
### Eval results