• Understand LangWatch’s role in LLM observability

• Install and configure LangWatch for real-time monitoring

• Track prompt inputs, responses, errors, and latency

• Connect LangWatch with LangChain and OpenAI APIs

• Analyze user sessions and trace prompt interactions

• Use filters and search tools for prompt debugging

• Visualize prompt-response chains and error logs

• Set up LangWatch dashboards and retention rules

• Identify model drift and inconsistencies

• Build auditable, testable, and transparent AI workflows

Course Syllabus

1. Introduction to LLM Monitoring and LangWatch

2. What is LangWatch and Why It Matters

3. Installing LangWatch and SDK Setup

4. Logging Prompt Inputs, Outputs, and Metadata

5. Connecting LangWatch to LangChain and OpenAI

6. Working with Real-Time Monitoring Dashboards

7. Debugging Prompt Chains and Failure Points

8. Analyzing User Sessions and Interaction Logs

9. Building Alerts and Searchable Logs

10. Visualizing Model Behavior and Response Timing

11. Ensuring Prompt Governance and Retention Policies

12. Case Study: Monitoring a Chatbot in Production

Upon completion of this course, you will earn a Uplatz Certificate of Completion verifying your knowledge of real-time monitoring and debugging for LLM systems using LangWatch. This certification demonstrates your readiness to manage AI quality assurance, interpret logs, and ensure reliability in prompt-driven applications. It’s ideal for developers, product managers, QA teams, and AI operators tasked with maintaining transparency and stability in AI-powered tools.

LangWatch enables essential observability for AI systems used in production. As more companies deploy LLMs in sensitive environments like customer service, education, or research, the demand for professionals who can maintain traceability and quality grows.

Career options include:

• AI QA Engineer

• Prompt Monitoring Analyst

• LLM Observability Specialist

• AI Product Support Engineer

• NLP Debugging Consultant

• AI Logging and Reliability Engineer

With LangWatch, you'll gain the confidence to manage LLM apps in production and contribute to ethical, reliable, and high-performing AI operations.

1. What is LangWatch?
   LangWatch is a monitoring tool that tracks prompt inputs, outputs, and behavior in LLM applications.

2. How does LangWatch improve LLM debugging?
   It logs prompt/response pairs, errors, and metadata, helping developers trace and fix issues.

3. Can LangWatch integrate with LangChain?
   Yes, it supports integration with LangChain to monitor chains and agents in real time.

4. What kind of data does LangWatch collect?
   Prompt text, responses, token usage, latency, error types, and user interaction history.

5. How does LangWatch differ from PromptLayer?
   LangWatch emphasizes real-time monitoring and filtering, while PromptLayer focuses more on versioning and analysis.

6. What is a session in LangWatch?
   A session is a traceable record of multiple prompts and responses associated with a single user or task.

7. How can LangWatch help QA teams?
   It allows teams to test and trace AI behavior before and after deployment, ensuring output consistency.

8. Does LangWatch support alerting?
   Yes, LangWatch can flag failed prompts, long latencies, or abnormal usage patterns.

9. What kind of models work with LangWatch?
   Any LLM-based application using APIs like OpenAI, Anthropic, or custom models.

10. Why is real-time logging important in AI applications?
    It allows fast detection of issues, transparent user experiences, and continuous improvement.