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Langfuse

Master Langfuse for tracing, analytics, and observability of LLM-powered applications to build reliable, debuggable, and production-ready AI systems.
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Course Duration: 10 Hours
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Langfuse – Observability & Analytics for LLM Applications – Online Course
 
Langfuse: Observability and Analytics for LLM Applications is a comprehensive, hands-on course designed for AI engineers, LLM developers, and product teams to monitor, debug, and optimize applications powered by Large Language Models (LLMs). As AI products move into production, tracing, evaluation, and performance monitoring become critical. This course equips you with the tools and knowledge to implement observability for AI systems using Langfuse.
 
At its core, Langfuse is an open-source observability and analytics platform for LLM applications. It enables developers to trace prompts, track costs, evaluate outputs, monitor latency, and debug complex AI workflows, offering deep insights into both chain-level and model-level performance. Unlike traditional observability tools, Langfuse is specifically built for AI-driven systems and integrates seamlessly with frameworks like LangChain, OpenAI API, and custom LLM pipelines.
 
This course introduces Langfuse fundamentals and progressively advances into real-world use cases, from debugging chatbots and analyzing prompt variations to monitoring token usage and integrating automated evaluations. You’ll learn how to create production-grade LLM monitoring pipelines, improve reliability, and iterate quickly in AI development workflows.
 
What is Langfuse?
 
Langfuse is an open-source observability platform for AI applications that provides granular tracing, logging, cost tracking, and evaluation tools tailored for LLM workflows. It helps teams visualize and debug LLM chains, analyze model responses, compare prompt versions, and optimize cost-performance tradeoffs—all within a single dashboard.
 
How to Use This Course Effectively
 
To get the best results from this course:
  1. Start with Fundamentals: Understand the core tracing and logging features before moving to advanced analytics.
  2. Instrument Your Code: Add Langfuse SDK to your chains, agents, or custom pipelines to capture traces.
  3. Use Visual Dashboards: Explore Langfuse’s UI to inspect requests, token costs, and latencies.
  4. Iteratively Debug: Identify slow steps, high-cost prompts, or failure points in LLM workflows.
  5. Experiment with Evaluations: Automate scoring of model outputs for quality assurance.
  6. Compare Variations: Run experiments across different prompts and models, analyzing performance.
  7. Monitor in Production: Set up Langfuse in deployed systems to track live performance and drift.
  8. Integrate with Tooling: Connect Langfuse with LangChain, OpenAI, or custom pipelines.
  9. Use Real-World Projects: Apply Langfuse to debug chatbots, RAG pipelines, and LLM APIs.
  10. Revisit and Optimize: Continuously refine your observability setup as your LLM apps evolve.
This course transforms you into a practitioner who can confidently monitor and debug AI-driven applications with Langfuse.

Course/Topic 1 - Coming Soon

  • The videos for this course are being recorded freshly and should be available in a few days. Please contact info@uplatz.com to know the exact date of the release of this course.

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Course Objectives Back to Top
By the end of this course, you will be able to:
 
  1. Understand the role of observability in LLMOps and AI application reliability.
  2. Install, configure, and use Langfuse for tracing and monitoring LLM workflows.
  3. Integrate Langfuse with LangChain, OpenAI API, and custom LLM systems.
  4. Trace prompts, token usage, and response latency for debugging.
  5. Evaluate model outputs with automated scoring and human review.
  6. Analyze prompt and model variations for performance optimization.
  7. Monitor deployed LLM applications for cost and quality metrics.
  8. Visualize AI workflows using Langfuse dashboards and analytics.
  9. Use Langfuse API for programmatic monitoring and automation.
  10. Apply observability best practices in production-grade LLM pipelines.
Course Syllabus Back to Top
Course Syllabus
 
Module 1: Introduction to Langfuse and LLM Observability
  • What is Langfuse?
  • Importance of Observability in AI Applications
  • Langfuse vs Traditional Logging Tools
Module 2: Getting Started with Langfuse
  • Setting Up Langfuse (Self-hosted & Cloud)
  • API Keys and SDK Integration
  • Langfuse UI Overview
Module 3: Tracing LLM Workflows
  • Capturing Traces in Langfuse
  • Step-by-Step Debugging of Chains and Agents
  • Visualizing Prompt Flow and Execution
Module 4: Token and Cost Analytics
  • Tracking Token Usage per Prompt and Chain
  • Cost Estimation for LLM Workflows
  • Optimizing Token Efficiency
Module 5: Performance Monitoring
  • Latency Analysis and Bottleneck Identification
  • Drift Detection in Model Outputs
  • Live Monitoring Dashboards
Module 6: Prompt and Model Evaluation
  • Automated Evaluation Pipelines
  • Human-in-the-Loop Feedback
  • Comparing Multiple Prompt Versions
Module 7: Integrations with AI Frameworks
  • Langfuse + LangChain
  • OpenAI API Instrumentation
  • Custom AI Pipeline Integration
Module 8: Debugging Complex AI Pipelines
  • RAG (Retrieval-Augmented Generation) Debugging
  • Tool and API Call Tracing
  • Handling Failures and Error Analysis
Module 9: Automation and API Usage
  • Using Langfuse API for Monitoring
  • Integrating into CI/CD Pipelines
  • Alerting and Notification Setup
Module 10: Real-World Projects
  • Debugging and Evaluating a Customer Support Chatbot
  • Token and Cost Optimization in Content Generation Apps
  • Monitoring Multi-Model AI Applications in Production
Module 11: Enterprise-Grade Langfuse
  • Scaling Langfuse for Large Teams
  • Role-Based Access Control (RBAC)
  • Advanced Security and Compliance
Module 12: Interview Questions and Career Path Guidance
Certification Back to Top

Upon completion, learners receive a Certificate of Completion from Uplatz, showcasing proficiency in LLM observability and monitoring using Langfuse. This certification validates expertise in tracing, debugging, performance monitoring, and evaluation of AI workflows. It demonstrates practical skills required by AI engineering teams to ensure reliability, cost-efficiency, and quality in LLM applications. Adding this credential to your portfolio strengthens credibility for AI-focused roles in engineering, operations, and DevOps.

Career & Jobs Back to Top
Langfuse expertise is in high demand as AI products move to production. Observability and debugging are critical for maintaining reliable and cost-efficient LLM workflows. Proficiency in Langfuse opens opportunities in roles such as:
  • AI/LLM Engineer
  • MLOps or LLMOps Engineer
  • AI Product Developer
  • AI Quality and Evaluation Specialist
  • Prompt Engineer (with Observability Focus)
These roles are highly relevant in SaaS, fintech, enterprise AI, consulting, and startups. Langfuse skills help professionals:
  • Debug token inefficiencies and reduce costs.
  • Monitor live AI systems with production-grade tools.
  • Evaluate model quality systematically for better user outcomes.
  • Collaborate across AI engineering and operations teams.
As observability becomes a core part of AI infrastructure, Langfuse knowledge positions you as a valuable contributor to building scalable, maintainable LLM applications.
Interview Questions Back to Top
  1. What is Langfuse, and why is it important for LLM applications?
    Langfuse is an open-source observability platform for LLM workflows that provides tracing, evaluation, and cost monitoring to improve reliability.
  2. How does Langfuse differ from LangSmith?
    Langfuse is open-source, focused on tracing and observability, while LangSmith emphasizes debugging and evaluation with closer LangChain integration.
  3. How do you integrate Langfuse into an LLM workflow?
    By adding Langfuse SDK/API calls into chain, agent, or pipeline code to capture traces, costs, and performance metrics.
  4. What data can Langfuse trace in an AI pipeline?
    It traces prompts, inputs/outputs, token counts, latency, tool invocations, and errors across the entire AI workflow.
  5. How can Langfuse help reduce costs in AI applications?
    By tracking token usage per prompt and identifying inefficient steps or verbose prompts consuming excessive tokens.
  6. What role does evaluation play in Langfuse?
    It supports automated output scoring, comparison across models, and human-in-the-loop feedback for quality assurance.
  7. Can Langfuse monitor production AI systems?
    Yes, it provides live dashboards, alerts, and analytics for monitoring latency, token costs, and drift in production.
  8. How does Langfuse handle RAG debugging?
    It traces retrieval calls and generation steps, allowing visibility into context relevance and response accuracy.
  9. Is Langfuse suitable for teams using non-LangChain frameworks?
    Yes, Langfuse integrates via APIs and SDKs into any LLM workflow, regardless of framework.
  10. What is drift detection in Langfuse?
    Drift detection identifies performance drops or unexpected changes in model outputs, prompting re-evaluation or retraining
Course Quiz Back to Top
Start Quiz
Q1. What are the payment options?
A1. We have multiple payment options: 1) Book your course on our webiste by clicking on Buy this course button on top right of this course page 2) Pay via Invoice using any credit or debit card 3) Pay to our UK or India bank account 4) If your HR or employer is making the payment, then we can send them an invoice to pay.

Q2. Will I get certificate?
A2. Yes, you will receive course completion certificate from Uplatz confirming that you have completed this course with Uplatz. Once you complete your learning please submit this for to request for your certificate https://training.uplatz.com/certificate-request.php

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Q16. Do you provide software access with video course?
A16. Software access can be purchased seperately at an additional cost. The cost varies from course to course but is generally in between GBP 20 to GBP 40 per month.

Q17. Does your course guarantee a job?
A17. Our course is designed to provide you with a solid foundation in the subject and equip you with valuable skills. While the course is a significant step toward your career goals, its important to note that the job market can vary, and some positions might require additional certifications or experience. Remember that the job landscape is constantly evolving. We encourage you to continue learning and stay updated on industry trends even after completing the course. Many successful professionals combine formal education with ongoing self-improvement to excel in their careers. We are here to support you in your journey!

Q18. Do you provide placement services?
A18. While our course is designed to provide you with a comprehensive understanding of the subject, we currently do not offer placement services as part of the course package. Our main focus is on delivering high-quality education and equipping you with essential skills in this field. However, we understand that finding job opportunities is a crucial aspect of your career journey. We recommend exploring various avenues to enhance your job search:
a) Career Counseling: Seek guidance from career counselors who can provide personalized advice and help you tailor your job search strategy.
b) Networking: Attend industry events, workshops, and conferences to build connections with professionals in your field. Networking can often lead to job referrals and valuable insights.
c) Online Professional Network: Leverage platforms like LinkedIn, a reputable online professional network, to explore job opportunities that resonate with your skills and interests.
d) Online Job Platforms: Investigate prominent online job platforms in your region and submit applications for suitable positions considering both your prior experience and the newly acquired knowledge. e.g in UK the major job platforms are Reed, Indeed, CV library, Total Jobs, Linkedin.
While we may not offer placement services, we are here to support you in other ways. If you have any questions about the industry, job search strategies, or interview preparation, please dont hesitate to reach out. Remember that taking an active role in your job search process can lead to valuable experiences and opportunities.

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Q21. Can I get help from a tutor if I have doubts while learning from a video course?
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