docs fix

docs/my-website/release_notes/v1.81.14.md

@@ -50,37 +50,31 @@ pip install litellm==1.81.14
 
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-## Guardrail Garden, Built-in Guardrails, and Compliance Playground
-
-Enterprise teams running AI in production keep hitting the same wall — they need to block investment advice, filter competitor mentions, catch insults, and they need to do it at the gateway level, not in application code. Today they use Azure Content Safety, tomorrow they want Bedrock Guardrails. And once a policy is live, they need to know if it's over-sensitive before users start complaining.
-
-This release brings the full workflow to LiteLLM: browse and deploy guardrails from the Guardrail Garden, use the new zero-cost built-in guardrails, and test everything in the Compliance Playground before it hits production.
 
 ### Guardrail Garden
 
-EasilyBrowse built-in and partner guardrails organized by use case — denied financial/legal/medical advice, harmful content detection, bias filters (gender, racial, religious), PII masking, prompt injection, and more. 
-
-Pick a guardrail, customize the parameters, and attach it to a team or key. Partner integrations include Presidio PII, Bedrock Guardrail, Lakera, OpenAI Moderation, Google Cloud Model Armor, and Guardrails AI.
+AI Platform Admins can now easily browse built-in and partner guardrails. The gateway level guardrails ar eoganized by use case, allowing you to easily get started with dthe right guardrail to sovle your problem - e.g Blocking Financial Advice, Blocking Insults, Blocking Competitor Mentions, etc.
 
 ![Guardrail Garden](../img/release_notes/guardrail_garden.png)
 
 ### 3 New Built-in Guardrails
 
-Three new guardrails that run directly on the gateway — no external API call, no extra cost:
+This release brings 3 new built-in guardrails that run directly on the gateway. This is great for AI Gateway Admins who need low latency, zero cost guardrails for their scenarios.
 
 - **Denied Financial Advice** — detects requests for personalized financial advice, investment recommendations, or financial planning
-- **Harmful Violence** — detects content related to violence, criminal planning, attacks, and violent threats
-- **Bias: Gender** — detects gender-based discrimination, stereotypes, and biased language
+- **Denied Insults** — detects insults, name-calling, and personal attacks directed at the chatbot, staff, or other people
+- **Competitor Name Blocker** — detects mentions of competitor brands in responses
 
-These join the existing built-in filters (Denied Legal Advice, Denied Medical Advice, Harmful Self-Harm, Harmful Child Safety, Harmful Illegal Weapons, Bias: Racial, Bias: Religious, and more). All are configurable per-team or per-key. You can swap in AWS Bedrock Guardrails or Azure Content Safety on the same endpoint without changing your application code — the governance layer stays on the gateway regardless of which provider you use.
+These guardrails are built for production and on our benchmarks had a 100% Recall and Precision.
 
 #### Eval results
 
-We benchmark every built-in guardrail against labeled datasets before shipping. Results for Denied Financial Advice (207 investment-question cases):
+We benchmark every built-in guardrail against labeled datasets before shipping. Results for Denied Financial Advice (207 cases) and Denied Insults (299 cases):
 
 | Guardrail | Precision | Recall | F1 | Latency p50 | Cost/req |
 |-----------|-----------|--------|----|-------------|----------|
 | Denied Financial Advice | 100% | 100% | 100% | <0.1ms | $0 |
+| Denied Insults | 100% | 100% | 100% | <0.1ms | $0 |
 
 For reference, ONNX embedding approaches on the same eval set hit 95–98% precision at 2–20ms latency and require additional dependencies. The built-in guardrails use no ML model — just structured YAML rules with layered matching — nothing to download, no API key, and latency is effectively zero.