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LLM Observability Training
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Drive Team Excellence with LLM Observability Corporate Training

LLM Observability is a critical discipline for organizations deploying large language models in production environments. This training equips participants with the tools, techniques, and frameworks needed to monitor, trace, evaluate, and govern LLM applications effectively. Participants explore the full spectrum of observability practices including prompt evaluation, hallucination detection, cost monitoring, RAG pipeline analysis, and responsible AI compliance to ensure reliable and trustworthy AI systems.

Edstellar's LLM Observability Instructor-led course offers virtual/onsite training options designed to meet the operational needs of modern AI teams. With a hands-on curriculum aligned to real-world production scenarios, this course empowers ML engineers, data scientists, and AI platform teams to build robust observability pipelines, reduce risk, and drive continuous improvement across their LLM deployments.

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Skills Your Employees Will Gain

These are the core, hands-on capabilities your team builds during the program.

  • LLM Request and Response Tracing
  • Prompt Quality Evaluation
  • Hallucination Detection and Mitigation
  • Latency and Token Cost Monitoring
  • RAG Pipeline Observability
  • Evaluation Framework Implementation
  • Responsible AI Governance and Compliance

What Your Team Will Achieve After This Training

  • Master end-to-end tracing and logging techniques for monitoring LLM requests and responses in production systems
  • Gain proficiency in evaluating prompt quality and detecting hallucinations using structured evaluation frameworks
  • Develop skills to monitor latency, token consumption, and infrastructure costs across LLM deployments
  • Learn to implement RAG pipeline observability and measure retrieval quality for augmented generation systems
  • Build alerting systems, dashboards, and incident response workflows tailored for LLM-based applications
  • Apply governance, compliance, and responsible AI monitoring practices to ensure safe and accountable LLM use

Topics & Program Outline

The curriculum is organized into focused modules built by industry experts and delivered virtually or on-premise. Interactive sessions reflect the evolving demands of the workplace, keeping the learning both relevant and practical.

  1. Introduction to LLM Observability
    • Definition and scope of LLM observability
    • Differences between traditional software monitoring and LLM monitoring
    • Key observability pillars: logs, traces, and metrics
    • Overview of the LLM application lifecycle
  2. Core Challenges in LLM Production Systems
    • Non-determinism and output variability in LLMs
    • Latency, reliability, and cost concerns at scale
    • Data drift and model degradation over time
    • Regulatory and compliance pressures for AI systems
  3. Observability Tooling Ecosystem
    • Overview of popular LLM observability platforms
    • Open-source versus commercial monitoring solutions
    • Integration patterns with existing MLOps stacks
    • Selecting tools based on team size and use case
  4. Establishing Observability Goals
    • Defining key performance indicators for LLM systems
    • Setting baseline metrics for quality and performance
    • Aligning observability objectives with business outcomes
    • Building an observability roadmap for AI teams
  5. Data Collection and Instrumentation Basics
    • Instrumenting LLM application code for observability
    • Capturing input prompts and output responses securely
    • Structuring metadata for downstream analysis
    • Handling sensitive data in observability pipelines
  6. Hands-On Lab: Setting Up a Basic Monitoring Environment
    • Configuring a local LLM monitoring stack
    • Connecting an LLM API to an observability backend
    • Capturing and visualizing first traces and logs
    • Reviewing initial metrics and identifying gaps
  1. Distributed Tracing Fundamentals
    • Concepts of spans, traces, and trace context propagation
    • OpenTelemetry standards for AI application tracing
    • Parent-child span relationships in LLM call chains
    • Trace sampling strategies for high-volume systems
  2. Logging Strategies for LLM Applications
    • Structured logging formats for prompt and response data
    • Log levels and when to apply them in LLM workflows
    • Centralized log aggregation with tools like ELK and Loki
    • Correlating logs with traces for root cause analysis
  3. Request and Response Capture Patterns
    • Middleware and interceptor patterns for LLM APIs
    • Capturing full prompt-response pairs with metadata
    • Handling streaming responses in observability pipelines
    • Anonymizing and redacting PII from captured data
  4. Multi-Step Chain Tracing
    • Tracing LangChain and similar orchestration frameworks
    • Visualizing multi-agent call graphs and dependencies
    • Identifying bottlenecks in chained LLM workflows
    • Propagating context across tool calls and retrievals
  5. Trace Storage and Retention Policies
    • Choosing storage backends for trace and log data
    • Defining retention periods based on compliance needs
    • Compressing and archiving historical trace data
    • Cost management for large-scale trace storage
  6. Hands-On Lab: Implementing End-to-End Tracing
    • Instrumenting a Python LLM application with OpenTelemetry
    • Visualizing traces in a Jaeger or Grafana Tempo backend
    • Correlating logs and traces across a multi-step chain
    • Analyzing trace data to identify performance issues
  1. Principles of Prompt Quality Assessment
    • Defining what constitutes a high-quality LLM prompt
    • Common prompt failure modes and their impact on outputs
    • Automated versus human evaluation of prompt effectiveness
    • Building a prompt quality rubric for your organization
  2. Quantitative Metrics for Prompt Evaluation
    • BLEU, ROUGE, and BERTScore for text similarity measurement
    • Faithfulness and relevance scoring for prompt outputs
    • Task-specific metrics for classification, summarization, and QA
    • Aggregating metrics into composite quality scores
  3. LLM-as-Judge Evaluation Patterns
    • Using a secondary LLM to score primary model outputs
    • Designing effective judge prompts and scoring rubrics
    • Managing bias and consistency in LLM judge evaluations
    • Calibrating judge scores against human annotations
  4. Prompt Versioning and Change Management
    • Tracking prompt versions alongside model versions
    • Regression testing prompts before production deployment
    • Documenting prompt intent and expected behavior
    • Rollback strategies for underperforming prompt updates
  5. Continuous Prompt Monitoring in Production
    • Setting up automated prompt quality checks in CI/CD
    • Alerting on quality metric degradation thresholds
    • Sampling strategies for cost-effective continuous evaluation
    • Dashboards for tracking prompt quality over time
  6. Hands-On Lab: Building a Prompt Evaluation Pipeline
    • Implementing BERTScore and faithfulness checks in Python
    • Setting up an LLM-as-judge scoring workflow
    • Logging prompt quality metrics to an observability backend
    • Creating a prompt quality dashboard in Grafana
  1. Understanding LLM Hallucinations
    • Types of hallucinations: intrinsic versus extrinsic
    • Root causes of hallucination in large language models
    • Business impact and risk scenarios for hallucinated outputs
    • Taxonomy of factual errors and confabulation patterns
  2. Automated Hallucination Detection Techniques
    • Grounding checks against retrieved context and knowledge bases
    • Consistency checking across multiple model responses
    • Fact-verification pipelines using external data sources
    • Confidence scoring and uncertainty quantification methods
  3. Output Validation Frameworks
    • Schema-based validation for structured LLM outputs
    • Using Guardrails AI and similar libraries for output control
    • Regex and rule-based filters for safety and compliance
    • Semantic similarity checks for output relevance validation
  4. Human-in-the-Loop Review Workflows
    • Designing escalation paths for low-confidence outputs
    • Sampling strategies for efficient human review at scale
    • Feedback collection interfaces for annotator workflows
    • Using human feedback to improve detection models
  5. Mitigation Strategies for Hallucination Reduction
    • Prompt engineering techniques to reduce hallucination rates
    • Retrieval augmentation as a grounding mechanism
    • Fine-tuning and RLHF for improving factual accuracy
    • Ensemble approaches for cross-validation of LLM outputs
  6. Hands-On Lab: Implementing a Hallucination Detection System
    • Building a grounding check pipeline against a knowledge base
    • Integrating Guardrails AI for output schema validation
    • Logging hallucination detection events to an observability system
    • Analyzing hallucination rates across different prompt templates
  1. Understanding LLM Latency Components
    • Time-to-first-token versus total generation latency
    • Network, queuing, and inference latency breakdown
    • Impact of prompt length and model size on latency
    • SLA definition and latency budget allocation for LLM services
  2. Token Usage Tracking and Analysis
    • Understanding input and output token counting mechanisms
    • Tracking token consumption per request, user, and feature
    • Identifying token-heavy prompts and optimization opportunities
    • Token usage trends and forecasting for capacity planning
  3. Cost Monitoring and Attribution
    • API cost calculation models for major LLM providers
    • Cost attribution by team, product, and feature
    • Setting cost budgets and automated spending alerts
    • Chargeback models for internal LLM platform teams
  4. Optimization Strategies for Cost and Latency
    • Prompt compression and token reduction techniques
    • Caching strategies for repeated or similar LLM queries
    • Model routing: selecting smaller models for simpler tasks
    • Batching requests to improve throughput and reduce costs
  5. Infrastructure-Level Performance Monitoring
    • GPU utilization and memory monitoring for self-hosted models
    • Autoscaling policies for variable LLM workloads
    • Queue depth and request backlog monitoring
    • Correlation of infrastructure metrics with application-level KPIs
  6. Hands-On Lab: Building a Cost and Latency Dashboard
    • Instrumenting an LLM application to capture token counts and latency
    • Sending metrics to Prometheus and visualizing in Grafana
    • Configuring cost alerts based on token usage thresholds
    • Analyzing cost-per-feature reports to identify savings opportunities
  1. RAG Architecture Overview
    • Components of a RAG system: retriever, context, and generator
    • Common RAG patterns: naive, advanced, and modular RAG
    • Failure modes specific to RAG pipelines
    • Observability requirements unique to retrieval-augmented systems
  2. Retrieval Quality Monitoring
    • Measuring retrieval precision, recall, and MRR at scale
    • Context relevance scoring for retrieved documents
    • Embedding drift detection for vector store maintenance
    • Query rewriting and its impact on retrieval quality
  3. Context Utilization Analysis
    • Measuring how effectively retrieved context is used by the LLM
    • Faithfulness of generated answers to retrieved context
    • Context window utilization and truncation monitoring
    • Identifying cases where retrieval hurts rather than helps
  4. Vector Store Health Monitoring
    • Index staleness detection and refresh scheduling
    • Monitoring embedding model performance over time
    • Query latency and throughput for vector search operations
    • Storage growth and cost management for vector databases
  5. End-to-End RAG Tracing
    • Tracing the full RAG pipeline from query to final response
    • Attributing errors to retrieval versus generation stages
    • Visualizing RAG pipeline performance in trace dashboards
    • Correlating retrieval quality with end-user satisfaction signals
  6. Hands-On Lab: RAG Pipeline Observability Implementation
    • Instrumenting a LangChain RAG pipeline with custom traces
    • Computing context relevance and faithfulness scores with RAGAS
    • Monitoring vector store query latency with Prometheus
    • Building a RAG quality dashboard with drill-down trace views
  1. Introduction to LLM Evaluation Frameworks
    • Role of evaluation frameworks in the LLM development lifecycle
    • Offline versus online evaluation approaches
    • Evaluation dataset construction and management
    • Comparing framework capabilities and use case fit
  2. RAGAS for RAG Evaluation
    • RAGAS metrics: faithfulness, answer relevance, context precision
    • Setting up RAGAS in a Python evaluation pipeline
    • Interpreting RAGAS scores and diagnosing low-performing queries
    • Integrating RAGAS into CI/CD for automated RAG quality gates
  3. LangSmith for LLM Tracing and Evaluation
    • LangSmith architecture: projects, datasets, and runs
    • Capturing and replaying LLM runs for debugging
    • Defining and running evaluators in LangSmith
    • Comparing model and prompt versions using LangSmith experiments
  4. Building Custom Evaluation Benchmarks
    • Designing domain-specific evaluation datasets for your use case
    • Creating custom scorers and evaluation functions in Python
    • Managing ground truth data and annotation workflows
    • Versioning and evolving benchmarks alongside model updates
  5. Evaluation Result Analysis and Reporting
    • Statistical analysis of evaluation results across model versions
    • Visualizing evaluation trends and regressions over time
    • Communicating evaluation findings to non-technical stakeholders
    • Using evaluation data to prioritize model improvement efforts
  6. Hands-On Lab: Running Multi-Framework Evaluations
    • Running RAGAS evaluation on a sample RAG pipeline output
    • Logging evaluation runs and comparing results in LangSmith
    • Building a custom Python evaluator for a domain-specific task
    • Generating an evaluation report with trend visualizations
  1. Designing LLM-Specific Alerting Rules
    • Identifying critical metrics that warrant automated alerts
    • Setting static and dynamic alert thresholds for LLM KPIs
    • Alert fatigue management and priority tiering
    • Multi-condition alert rules for complex failure scenarios
  2. Building Effective LLM Monitoring Dashboards
    • Dashboard design principles for LLM production systems
    • Key panels: latency, cost, quality, error rate, and token usage
    • Using Grafana, Datadog, or LangSmith for dashboard creation
    • Role-specific dashboard views for engineers and business stakeholders
  3. Anomaly Detection for LLM Metrics
    • Statistical methods for detecting metric anomalies
    • ML-based anomaly detection for LLM performance signals
    • Seasonality and baseline adjustment for accurate alerting
    • Correlating anomalies across multiple LLM metrics simultaneously
  4. Incident Response Workflows for LLM Systems
    • Defining incident severity levels for LLM-related failures
    • On-call runbooks and escalation procedures for AI incidents
    • Root cause analysis techniques for LLM production issues
    • Post-incident review and corrective action documentation
  5. Notification and On-Call Integration
    • Integrating alerts with PagerDuty, Opsgenie, and Slack
    • On-call rotation scheduling for LLM platform teams
    • Alert suppression and maintenance window configuration
    • Automating initial triage actions with runbook automation
  6. Hands-On Lab: End-to-End Alerting and Incident Simulation
    • Configuring Prometheus alert rules for LLM quality degradation
    • Building a Grafana dashboard with key LLM production panels
    • Simulating a latency spike and executing an incident response runbook
    • Conducting a post-incident review using trace and log data
  1. Principles of A/B Testing for LLM Applications
    • Designing controlled experiments for prompt and model changes
    • Statistical significance and sample size requirements for LLM tests
    • Defining success metrics and guardrail metrics for experiments
    • Ethical considerations in A/B testing AI-generated content
  2. Traffic Splitting and Feature Flag Strategies
    • Implementing percentage-based traffic routing for LLM variants
    • Feature flags for safe rollout of prompt and model updates
    • Canary deployments for gradual LLM model rollouts
    • Shadow testing: running new models in parallel without user impact
  3. Online Evaluation and User Feedback Collection
    • Implicit signals: click-through, session length, and task completion
    • Explicit feedback: thumbs up/down and rating collection
    • Linking user feedback signals to specific LLM traces
    • Feedback loop design for continuous model improvement
  4. Continuous Evaluation Pipeline Architecture
    • Integrating automated evaluation into CI/CD pipelines
    • Scheduled batch evaluation jobs for production LLM outputs
    • Dataset management for continuous evaluation benchmarks
    • Automated promotion and rollback based on evaluation results
  5. Experiment Result Analysis and Decision Making
    • Bayesian versus frequentist approaches to experiment analysis
    • Handling novelty effects and selection bias in LLM experiments
    • Multi-metric decision frameworks for model selection
    • Documenting experiment results and learnings for team knowledge
  6. Hands-On Lab: Building a Continuous Evaluation Pipeline
    • Setting up an A/B test between two prompt variants in Python
    • Collecting and analyzing automated evaluation metrics per variant
    • Integrating evaluation checks into a GitHub Actions CI workflow
    • Making a data-driven promotion decision based on experiment results
  1. AI Governance Frameworks and Standards
    • Overview of AI governance frameworks: NIST AI RMF, ISO 42001
    • Regulatory landscape: EU AI Act, GDPR, and sector-specific rules
    • Organizational AI governance structures and responsibilities
    • Mapping governance requirements to observability capabilities
  2. Bias Detection and Fairness Monitoring
    • Defining fairness metrics for LLM outputs across demographic groups
    • Automated bias detection in LLM response monitoring
    • Disparate impact analysis for LLM-driven decisions
    • Mitigation strategies and bias audit reporting
  3. Content Safety and Toxicity Monitoring
    • Implementing content moderation filters for LLM outputs
    • Toxicity scoring using Perspective API and open-source classifiers
    • Monitoring jailbreak attempts and adversarial prompt patterns
    • Escalation workflows for detected unsafe content
  4. Data Privacy and Compliance in Observability
    • PII detection and redaction in captured prompt and response data
    • Data residency and sovereignty requirements for observability logs
    • Audit trail creation for regulatory compliance reporting
    • Consent management and data subject rights in AI systems
  5. Responsible AI Monitoring Practices
    • Explainability requirements and logging for high-stakes LLM decisions
    • Human oversight mechanisms and override logging
    • Monitoring for model card compliance and capability drift
    • Continuous responsible AI auditing and reporting cadences
  6. Hands-On Lab: Implementing a Governance Monitoring System
    • Integrating a toxicity scorer into an LLM response pipeline
    • Building a PII detection and redaction layer for observability data
    • Creating an audit log dashboard for compliance reporting
    • Generating a responsible AI monitoring report for a sample deployment

Who Should Attend?

This program suits professionals at many levels across the organization, including:

  • ML Engineers
  • AI/LLM Application Developers
  • Data Scientists
  • MLOps and DevOps Engineers
  • AI Product Managers
  • Data and AI Platform Architects

What are the Prerequisites?

Professionals should have basic knowledge of machine learning concepts and Python programming to take the LLM Observability training course.

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        Hear from Organizations We've Trained

        "Edstellar's virtual LLM Observability training transformed how our AI team monitors production systems. Within weeks of completing the program, we reduced mean time to detect LLM quality issues by 60% and cut unnecessary token costs by 25%. The hands-on labs were directly applicable to our stack, and the instructor's depth of knowledge was outstanding."

        Rohan Mehta

        VP of AI Engineering,

        A Leading Fintech Enterprise

        "The onsite LLM Observability training delivered by Edstellar was exactly what our platform engineering team needed. The trainers worked through real scenarios from our RAG-based product, and we left with a fully functional observability pipeline. We have since reduced hallucination incidents in production by over 40% and improved our SLA compliance significantly."

        Priya Sundaram

        Head of AI Platform,

        A Global Insurance Group

        "We sent our core AI team through Edstellar's intensive LLM Observability program and the results were immediate. The structured curriculum covering tracing, evaluation frameworks, and governance gave our engineers a shared language and systematic approach. Post-training, our evaluation coverage increased from 20% to 85% of production LLM calls within a single quarter."

        Ananya Krishnan

        Director of Data Science,

        A Multinational Retail Corporation

        "Edstellar's IT & Technical training programs have been instrumental in strengthening our engineering teams and building future-ready capabilities. The hands-on approach, practical cloud scenarios, and expert guidance helped our teams improve technical depth, problem-solving skills, and execution across multiple projects. We're excited to extend more of these impactful programs to other business units."

        Aditi Rao

        L&D Head,

        A Global Technology Company

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