Quick quickstart updates

docs/index.mdx

@@ -28,7 +28,23 @@ import { RegionalUrl } from "@site/src/components/RegionalUrls";
 
 **LangSmith** is a platform for building production-grade LLM applications.
 It allows you to closely monitor and evaluate your application, so you can ship quickly and with confidence.
-LangChain's open source frameworks [langchain](https://python.langchain.com) and [langgraph](https://langchain-ai.github.io/langgraph/) work seemlessly with LangSmith but are not necessary - LangSmith works on its own!
+With LangSmith you can:
+
+- **Trace LLM Applications**: Gain visibility into LLM calls and other parts of your application's logic.
+- **Evaluate Performance**: Compare results across models, prompts, and architectures to identify what works best.
+- **Improve Prompts**: Quickly refine prompts to achieve more accurate and reliable results.
+
+:::tip LangSmith + LangChain OSS
+
+LangSmith integrates seamlessly with LangChain's open source frameworks [`langchain`](https://python.langchain.com) and [`langgraph`](https://langchain-ai.github.io/langgraph/), with no extra instrumentation needed.
+
+If you're already using either of these, see the how-to guide for [setting up LangSmith with LangChain](./observability/how_to_guides/tracing/trace_with_langchain) or [setting up LangSmith with LangGraph](https://docs.smith.langchain.com/observability/how_to_guides/tracing/trace_with_langgraph).
+
+:::
+
+LangSmith is a **standalone platform** that can be used on it's own no matter how you're creating your LLM applicatons.
+
+In this tutorial, we'll walk you though logging your first trace in LangSmith using the LangSmith SDK and running an evaluation to measure the performance of your application. This example uses the OpenAI API, however you can use your provider of choice.
 
 ## 1. Install LangSmith
 
@@ -60,12 +76,6 @@ To create an API key head to the <RegionalUrl text='Settings page' suffix='/sett
 
 ## 4. Log your first trace
 
-:::tip LangSmith + LangChain OSS
-You don't need to use the LangSmith SDK directly if your application is built on [LangChain](https://python.langchain.com)/[LangGraph](https://langchain-ai.github.io/langgraph/) (either Python and JS).
-
-See the how-to guide for tracing with LangChain [here](./observability/how_to_guides/tracing/trace_with_langchain).
-:::
-
 We provide multiple ways to log traces to LangSmith. Below, we'll highlight
 how to use `traceable()`. See more on the [Annotate code for tracing](./observability/how_to_guides/tracing/annotate_code) page.
 
@@ -87,12 +97,17 @@ how to use `traceable()`. See more on the [Annotate code for tracing](./observab
   groupId="client-language"
 />
 
-- View a [sample output trace](https://smith.langchain.com/public/b37ca9b1-60cd-4a2a-817e-3c4e4443fdc0/r).
-- Learn more about tracing in the observability [tutorials](./observability/tutorials), [conceptual guide](./observability/concepts) and [how-to guides](./observability/how_to_guides/index.md).
+Learn more about tracing in the observability [tutorials](./observability/tutorials), [conceptual guide](./observability/concepts) and [how-to guides](./observability/how_to_guides/index.md).
+
+## 5. View your trace
+
+By default, the trace will be logged to the project with the name `default`. You should see the following [sample output trace](https://smith.langchain.com/public/b37ca9b1-60cd-4a2a-817e-3c4e4443fdc0/r) logged using the above code.
+
+## 6. Run your first evaluation
 
-## 5. Run your first evaluation
+[Evaluations](./evaluation/concepts) help assess application performance by testing the application against a given set of inputs. Evaluations require a system to test, data to serve as test cases, and evaluators to grade the results.
 
-Evaluation requires a system to test, data to serve as test cases, and optionally evaluators to grade the results. Here we use a built-in accuracy evaluator.
+Here we are running an evaluation against a sample dataset using a simple custom evaluator that checks if the real output exactly matches our gold-standard output.
 
 <CodeTabs
   tabs={[

src/components/QuickStart.js

@@ -154,14 +154,13 @@
   );
 }
 
 export function ConfigureSDKEnvironmentCodeTabs({}) {
   return (
     <CodeTabs
       tabs={[
         ShellBlock(`export LANGCHAIN_TRACING_V2=true
 export LANGCHAIN_API_KEY=<your-api-key>
 
-# The below examples use the OpenAI API, though it's not necessary in general
 export OPENAI_API_KEY=<your-openai-api-key>`),
       ]}
       groupId="client-language"
@@ -169,7 +168,7 @@
   );
 }
 
 export function ConfigureEnvironmentCodeTabs({}) {
   return (
     <CodeTabs
       tabs={[
@@ -184,7 +183,7 @@
   );
 }
 
 export function LangChainQuickStartCodeTabs({}) {
   const simpleTSBlock = `import { ChatOpenAI } from "@langchain/openai";
 import { ChatPromptTemplate } from "@langchain/core/prompts";
 import { StringOutputParser } from "@langchain/core/output_parsers";
@@ -202,7 +201,7 @@
 const context = "During this morning's meeting, we solved all world conflict."
 await chain.invoke({ question: question, context: context });`;
 
   const alternativeTSBlock = `import { Client } from "langsmith";
 import { LangChainTracer } from "langchain/callbacks";
 
 const client = new Client({
@@ -316,7 +315,7 @@
     print(tok, end="")
 # See an example run at: https://smith.langchain.com/public/3e853ad8-77ce-404d-ad4c-05726851ad0f/r`);
 
 export function TraceableQuickStartCodeBlock({}) {
   return (
     <CodeBlock
       className={TraceableQuickStart.value}
@@ -327,7 +326,7 @@
   );
 }
 
 export function TraceableThreadingCodeBlock({}) {
   return (
     <CodeBlock
       className={TraceableQuickStart.value}
@@ -401,7 +400,7 @@
   );
 }
 
 export function RunTreeQuickStartCodeTabs({}) {
   return (
     <CodeTabs
       tabs={[