Langfuse
Master Langfuse for tracing, analytics, and observability of LLM-powered applications to build reliable, debuggable, and production-ready AI systems.Preview Langfuse course
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- Start with Fundamentals: Understand the core tracing and logging features before moving to advanced analytics.
- Instrument Your Code: Add Langfuse SDK to your chains, agents, or custom pipelines to capture traces.
- Use Visual Dashboards: Explore Langfuse’s UI to inspect requests, token costs, and latencies.
- Iteratively Debug: Identify slow steps, high-cost prompts, or failure points in LLM workflows.
- Experiment with Evaluations: Automate scoring of model outputs for quality assurance.
- Compare Variations: Run experiments across different prompts and models, analyzing performance.
- Monitor in Production: Set up Langfuse in deployed systems to track live performance and drift.
- Integrate with Tooling: Connect Langfuse with LangChain, OpenAI, or custom pipelines.
- Use Real-World Projects: Apply Langfuse to debug chatbots, RAG pipelines, and LLM APIs.
- Revisit and Optimize: Continuously refine your observability setup as your LLM apps evolve.
Course/Topic 1 - Coming Soon
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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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Understand the role of observability in LLMOps and AI application reliability.
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Install, configure, and use Langfuse for tracing and monitoring LLM workflows.
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Integrate Langfuse with LangChain, OpenAI API, and custom LLM systems.
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Trace prompts, token usage, and response latency for debugging.
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Evaluate model outputs with automated scoring and human review.
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Analyze prompt and model variations for performance optimization.
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Monitor deployed LLM applications for cost and quality metrics.
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Visualize AI workflows using Langfuse dashboards and analytics.
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Use Langfuse API for programmatic monitoring and automation.
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Apply observability best practices in production-grade LLM pipelines.
- What is Langfuse?
- Importance of Observability in AI Applications
- Langfuse vs Traditional Logging Tools
- Setting Up Langfuse (Self-hosted & Cloud)
- API Keys and SDK Integration
- Langfuse UI Overview
- Capturing Traces in Langfuse
- Step-by-Step Debugging of Chains and Agents
- Visualizing Prompt Flow and Execution
- Tracking Token Usage per Prompt and Chain
- Cost Estimation for LLM Workflows
- Optimizing Token Efficiency
- Latency Analysis and Bottleneck Identification
- Drift Detection in Model Outputs
- Live Monitoring Dashboards
- Automated Evaluation Pipelines
- Human-in-the-Loop Feedback
- Comparing Multiple Prompt Versions
- Langfuse + LangChain
- OpenAI API Instrumentation
- Custom AI Pipeline Integration
- RAG (Retrieval-Augmented Generation) Debugging
- Tool and API Call Tracing
- Handling Failures and Error Analysis
- Using Langfuse API for Monitoring
- Integrating into CI/CD Pipelines
- Alerting and Notification Setup
- Debugging and Evaluating a Customer Support Chatbot
- Token and Cost Optimization in Content Generation Apps
- Monitoring Multi-Model AI Applications in Production
- Scaling Langfuse for Large Teams
- Role-Based Access Control (RBAC)
- Advanced Security and Compliance
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.
- AI/LLM Engineer
- MLOps or LLMOps Engineer
- AI Product Developer
- AI Quality and Evaluation Specialist
- Prompt Engineer (with Observability Focus)
- 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.
- 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. - 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. - 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. - 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. - 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. - What role does evaluation play in Langfuse?
It supports automated output scoring, comparison across models, and human-in-the-loop feedback for quality assurance. - Can Langfuse monitor production AI systems?
Yes, it provides live dashboards, alerts, and analytics for monitoring latency, token costs, and drift in production. - How does Langfuse handle RAG debugging?
It traces retrieval calls and generation steps, allowing visibility into context relevance and response accuracy. - Is Langfuse suitable for teams using non-LangChain frameworks?
Yes, Langfuse integrates via APIs and SDKs into any LLM workflow, regardless of framework. - What is drift detection in Langfuse?
Drift detection identifies performance drops or unexpected changes in model outputs, prompting re-evaluation or retraining