One of the most powerful applications enabled by LLMs is sophisticated question-answering (Q&A) chatbots. These are applications that can answer questions about specific source information. These applications use a technique known as Retrieval Augmented Generation, or RAG.
This guide assumes familiarity with the following concepts:
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Dashboards give you high‑level insights into your trace data, helping you spot trends and monitor the health of your applications. LangSmith offers two dashboard types:
Tools allow language models to interact with external systems and perform actions beyond just generating text. In the LangSmith playground, you can use two types of tools:
LangSmith provides a collaborative interface to create, test, and iterate on prompts.
After running an experiment, you can use LangSmith's experiment view to analyze the results and draw insights about your experiment's performance.
Oftentimes, when you are iterating on your LLM application (such as changing the model or the prompt), you will want to compare the results of different experiments.
LangSmith supports OpenTelemetry-based tracing, allowing you to send traces from any OpenTelemetry-compatible application. This guide covers both automatic instrumentation for LangChain applications and manual instrumentation for other frameworks.
Google Cloud Firestore is a serverless document-oriented database that scales to meet any demand. Extend your database application to build AI-powered experiences leveraging Firestore's Langchain integrations.
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