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diff --git a/frontend/components/landing/landing.tsx b/frontend/components/landing/landing.tsx
index 0a80af54..572f17e0 100644
--- a/frontend/components/landing/landing.tsx
+++ b/frontend/components/landing/landing.tsx
@@ -39,7 +39,9 @@ const sections: Section[] = [
{
id: 'traces',
title: 'Trace',
- description: 'Tracing your LLM application provides visibility into execution steps while collecting valuable data for evaluations, few-shot examples, and fine-tuning.',
+ description: `Tracing your LLM application provides visibility into every
+ execution step while collecting valuable data for evaluations, few-shot examples, and fine-tuning.
+ With Laminar, you can start tracing with just 2 lines of code.`,
pythonCodeExample: `from lmnr import Laminar, observe
# automatically traces common LLM frameworks and SDKs
@@ -47,7 +49,9 @@ Laminar.initialize(project_api_key="...")
@observe() # you can also manually trace any function
def my_function(...):
-...`,
+ ...
+
+`,
tsCodeExample: `import { Laminar, observe } from '@lmnr-ai/lmnr';
// automatically traces common LLM frameworks and SDKs
@@ -64,7 +68,9 @@ const myFunction = observe({name: 'myFunc'}, async () => {
{
id: 'evals',
title: 'Evaluate',
- description: 'Evaluations are unit tests for your LLM application. They help you answer questions like "Did my last change improve the performance?". Run custom evals via code, CLI, or CI/CD pipeline.',
+ description: `Evaluations are unit tests for your LLM application.
+ They help you answer questions like "Did my last change improve the performance?".
+ With Laminar, you can run custom evals via code, CLI, or CI/CD pipeline.`,
image: evals,
pythonCodeExample: `from lmnr import evaluate
@@ -90,7 +96,8 @@ evaluate({
{
id: 'labels',
title: 'Label',
- description: 'Label LLM outputs to identify successes and failures. Build datasets for fine-tuning, prompt examples, and targeted improvements. Use human labels for evaluations.',
+ description: `With Laminar, you can label LLM outputs to identify successes and failures.
+ Build datasets for fine-tuning and few-shot examples. Use human labels as evaluation scores.`,
image: labels,
docsLink: 'https://docs.lmnr.ai/labels/introduction',
pythonCodeExample: `from lmnr import Laminar
@@ -101,6 +108,7 @@ with Laminar.with_labels(my_labels):
openai_client.chat.completions.create(
messages=[ ... ]
)
+
`,
tsCodeExample: `import { Laminar, withLabels} from '@lmnr-ai/lmnr';
@@ -221,27 +229,27 @@ export default function Landing() {