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Maddy Adams
65 lines
3.9 KiB
Plaintext
65 lines
3.9 KiB
Plaintext
---
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sidebar_label: Get Started
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sidebar_position: 1
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table_of_contents: true
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---
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# Get started with LangSmith
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**LangSmith** is a platform for building production-grade LLM applications.
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It allows you to closely monitor and evaluate your application, so you can ship quickly and with confidence.
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<h3 style={{ color: '#1a73e8', marginTop: 0 }}>[Observability](./observability)</h3>
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Analyze traces in LangSmith and configure metrics, dashboards, alerts based on these.
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<h3 style={{ color: '#1a73e8', marginTop: 0 }}>[Evals](./evaluation)</h3>
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Evaluate your application over production traffic — score application performance and get human feedback on your data.
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<h3 style={{ color: '#1a73e8', marginTop: 0 }}>[Prompt Engineering](./prompt_engineering/quickstarts/quickstart_ui)</h3>
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Iterate on prompts, with automatic version control and collaboration features.
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</div>
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:::tip LangSmith + LangChain OSS
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LangSmith is framework-agnostic — it can be used with or without LangChain's open source frameworks [`langchain`](https://python.langchain.com) and [`langgraph`](https://langchain-ai.github.io/langgraph/).
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If you are using either of these, you can enable LangSmith tracing with a single environment variable.
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For more see the how-to guide for [setting up LangSmith with LangChain](./observability/how_to_guides/trace_with_langchain) or [setting up LangSmith with LangGraph](https://docs.smith.langchain.com/observability/how_to_guides/tracing/trace_with_langgraph).
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:::
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## Observability
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Observability is important for any software application, but especially so for LLM applications. LLMs are non-deterministic by nature, meaning they can produce unexpected results. This makes them trickier than normal to debug.
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This is where LangSmith can help! LangSmith has LLM-native observability, allowing you to get meaningful insights from your application. LangSmith’s observability features have you covered throughout all stages of application development - from prototyping, to beta testing, to production.
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- Get started by [adding tracing](./observability) to your application.
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- [Create dashboards](./observability/how_to_guides/dashboards) to view key metrics like RPS, error rates and costs.
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## Evals
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The quality and development speed of AI applications depends on high-quality evaluation datasets and metrics to test and optimize your applications on. The LangSmith SDK and UI make building and running high-quality evaluations easy.
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- Get started by [creating your first evaluation](./evaluation).
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- Quickly assess the performance of your application using our [off-the-shelf evaluators](https://docs.smith.langchain.com/evaluation/how_to_guides/prebuilt_evaluators) as a starting point.
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- [Analyze results](./evaluation/how_to_guides#analyzing-experiment-results) of evaluations in the LangSmith UI and [compare results](https://docs.smith.langchain.com/evaluation/how_to_guides/compare_experiment_results) over time.
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- Easily collect [human feedback](./evaluation/how_to_guides#annotation-queues-and-human-feedback) on your data to improve your application.
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## Prompt Engineering
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While traditional software applications are built by writing code, AI applications involve writing prompts to instruct the LLM on what to do. LangSmith provides a set of tools designed to enable and facilitate prompt engineering to help you find the perfect prompt for your application.
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- Get started by [creating your first prompt](./prompt_engineering/how_to_guides/create_a_prompt).
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- Iterate on models and prompts using the [Playground](./prompt_engineering/how_to_guides#playground).
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- [Manage prompts programmatically](https://docs.smith.langchain.com/prompt_engineering/how_to_guides/prompts/manage_prompts_programatically) in your application.
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