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LangSmith: Observability & Evaluation for LLM Apps
Master LangSmith to debug, monitor,& evaluate Large Language Model applications with structured traces, real-time analytics & custom feedback loops.
Course Duration: 10 Hours
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LangSmith – Observability & Evaluation for LLM Apps – Online Course
LangSmith: Observability & Evaluation for LLM Apps is a specialized, self-paced course designed for AI developers, data scientists, and prompt engineers building production-grade applications using LLMs (Large Language Models). This course introduces learners to LangSmith, a powerful observability platform developed by LangChain, that enables tracing, debugging, evaluation, and human feedback collection for complex AI workflows.
Course Introduction
As AI and LLM-based apps transition from experimentation to production, there is a growing need for tools that provide transparency, reproducibility, and reliability. LangSmith is at the forefront of this movement, offering robust tools for monitoring LLM behavior, logging intermediate steps, collecting structured evaluations, and rapidly iterating with confidence.
As AI and LLM-based apps transition from experimentation to production, there is a growing need for tools that provide transparency, reproducibility, and reliability. LangSmith is at the forefront of this movement, offering robust tools for monitoring LLM behavior, logging intermediate steps, collecting structured evaluations, and rapidly iterating with confidence.
What is LangSmith?
LangSmith is a developer platform by LangChain for observability and evaluation of applications powered by Large Language Models. It allows developers to trace execution flows, log inputs/outputs at each step, benchmark different LLM chains, and systematically test prompt performance across datasets.
LangSmith is a developer platform by LangChain for observability and evaluation of applications powered by Large Language Models. It allows developers to trace execution flows, log inputs/outputs at each step, benchmark different LLM chains, and systematically test prompt performance across datasets.
This course takes you through LangSmith’s core functionalities—from simple tracing and logging to automated evaluation pipelines using human or model-generated feedback. You’ll learn to integrate LangSmith with LangChain apps, collect analytics, and debug failures with deep visibility into chain-of-thought logic.
How to Use This Course
Whether you're building agents, RAG pipelines, or prompt chains, this course equips you with practical tools and use cases. To get the most out of it:
Whether you're building agents, RAG pipelines, or prompt chains, this course equips you with practical tools and use cases. To get the most out of it:
- Start with basic concepts like tracing and span visualization.
- Build hands-on projects to test LangSmith integrations with LangChain.
- Use real datasets and test cases to evaluate prompt and model performance.
- Practice human-in-the-loop evaluations for reliability testing.
- Monitor real-time analytics for deployed LLM apps.
The course is structured to take you from first trace to full production observability using LangSmith.
Course Objectives Back to Top
By the end of this course, you will be able to:
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Understand the role of observability in LLM application development.
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Set up LangSmith with LangChain-based Python applications.
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Use LangSmith tracing to monitor each step of multi-component chains.
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Perform local and remote debugging using traces and logs.
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Create custom feedback functions and evaluations.
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Benchmark prompt versions across multiple runs and datasets.
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Conduct human-in-the-loop and model-in-the-loop evaluations.
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Visualize chain behavior with structured span trees.
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Integrate LangSmith with CI/CD pipelines for continuous evaluation.
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Use LangSmith for production monitoring of live LLM apps.
Course Syllabus Back to Top
Course Syllabus
Module 1: Introduction to LangSmith
- What is LangSmith and why use it?
- Key features: tracing, feedback, evaluation
- Installing and setting up LangSmith SDK
Module 2: LangSmith Fundamentals
- Tracing chains and agents
- Spans, parent-child relations, and visualizations
- Exploring LangSmith UI and trace logs
Module 3: Setting Up LangChain Integration
- Connecting LangChain to LangSmith
- Tracing tool usage in Python
- Debugging workflows in LangChain apps
Module 4: Data Traces and Logs
- Capturing inputs, outputs, and metadata
- Logging intermediate steps
- Filtering and searching through traces
Module 5: Custom Feedback Functions
- Writing feedback logic with Python
- Score-based vs binary feedback
- Collecting structured feedback on model outputs
Module 6: Evaluation Datasets
- Creating datasets from prompts and responses
- Using evaluation suites to test model performance
- Dataset versioning and reproducibility
Module 7: Model and Human Evaluation
- LLM-as-a-judge methods
- Human-in-the-loop interfaces
- Comparative evaluation across prompt chains
Module 8: Building Evaluation Pipelines
- Automating evaluation using LangSmith
- Using CI/CD for regression testing
- Scoring models across test sets
Module 9: Production Monitoring
- Real-time analytics and alerting
- Monitoring performance drift
- Visualizing usage and tracing errors
Modules 10–12: LangSmith Projects
- Evaluating a RAG pipeline with LangSmith
- Tracing a multi-agent system
- Setting up continuous evaluation for prompt experiments
Module 13: LangSmith Interview Questions & Answers
Certification Back to Top
Upon successful completion of the LangSmith: Observability & Evaluation for LLM Apps course, you will receive a Certificate of Completion from Uplatz, verifying your proficiency in debugging, evaluating, and managing LLM applications using LangSmith. This certificate is valuable for AI developers, ML engineers, and teams deploying GPT-based apps, especially those aiming to ensure quality, safety, and performance. It signals both practical skills and architectural understanding in building transparent, trustworthy LLM applications.
Career & Jobs Back to Top
production. Mastery of LangSmith opens new career paths in AI development, especially where reliability, explainability, and user trust are essential.
By completing this course, you can pursue roles such as:
- LLM Engineer
- Prompt Engineer
- AI Product Developer
- AI Evaluator / QA Specialist
- Applied ML Engineer
- ML Ops / AI Infrastructure Engineer
LangSmith is widely used by teams building customer support bots, retrieval-augmented generation (RAG) systems, autonomous agents, and document-processing pipelines. With the rise of responsible and production-grade AI, professionals who can monitor and evaluate AI outputs are in high demand. This course empowers you with essential skills to contribute meaningfully in this domain.
Interview Questions Back to Top
1. What is LangSmith and how does it support LLM applications?
LangSmith is a developer tool for tracing, debugging, evaluating, and monitoring applications that use LLMs. It gives visibility into chain execution and enables structured feedback collection.
LangSmith is a developer tool for tracing, debugging, evaluating, and monitoring applications that use LLMs. It gives visibility into chain execution and enables structured feedback collection.
2. How does tracing work in LangSmith?
LangSmith records every function call (span) in an LLM chain, displaying inputs, outputs, metadata, and nested logic. This helps developers trace bugs and understand behavior step-by-step.
LangSmith records every function call (span) in an LLM chain, displaying inputs, outputs, metadata, and nested logic. This helps developers trace bugs and understand behavior step-by-step.
3. What are feedback functions in LangSmith?
Feedback functions are scripts that assign scores or labels to model outputs, either manually or automatically. They help evaluate the quality or correctness of results.
Feedback functions are scripts that assign scores or labels to model outputs, either manually or automatically. They help evaluate the quality or correctness of results.
4. Can LangSmith be used with models other than OpenAI?
Yes. LangSmith is model-agnostic. It works with any LLM used in LangChain-based apps, including Anthropic, Cohere, Hugging Face, and others.
Yes. LangSmith is model-agnostic. It works with any LLM used in LangChain-based apps, including Anthropic, Cohere, Hugging Face, and others.
5. How does LangSmith handle human evaluation?
LangSmith supports human feedback collection through interfaces and APIs, allowing annotators to score or compare LLM outputs across tasks.
LangSmith supports human feedback collection through interfaces and APIs, allowing annotators to score or compare LLM outputs across tasks.
6. What is the benefit of using datasets in LangSmith?
Datasets help run batch evaluations, compare model or prompt versions, and track performance over time—supporting reproducibility and regression testing.
Datasets help run batch evaluations, compare model or prompt versions, and track performance over time—supporting reproducibility and regression testing.
7. What is a span in LangSmith’s trace log?
A span represents a unit of execution—e.g., an LLM call, tool invocation, or function output. Spans can be nested to reflect structured flow.
A span represents a unit of execution—e.g., an LLM call, tool invocation, or function output. Spans can be nested to reflect structured flow.
8. How can LangSmith integrate with CI/CD?
LangSmith supports automated evaluation in CI/CD pipelines, allowing teams to run performance tests on prompts and chains before deployment.
LangSmith supports automated evaluation in CI/CD pipelines, allowing teams to run performance tests on prompts and chains before deployment.
9. What are typical use cases of LangSmith in production?
LangSmith is used for monitoring chatbot interactions, evaluating retrieval responses, auditing sensitive content, and optimizing prompt performance.
LangSmith is used for monitoring chatbot interactions, evaluating retrieval responses, auditing sensitive content, and optimizing prompt performance.
10. How does LangSmith improve developer productivity?
By offering detailed traces and evaluation tools, LangSmith helps developers debug faster, test more reliably, and iterate on LLM pipelines with confidence.
By offering detailed traces and evaluation tools, LangSmith helps developers debug faster, test more reliably, and iterate on LLM pipelines with confidence.
Course Quiz Back to Top
FAQs
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