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Duration 35 hours
Course Outline
Foundations of Diagnosis and Introduction
- Analysis of failure modes in LLM systems and specific challenges related to Ollama
- Setting up reproducible experiments and controlled testing environments
- Debugging toolkit: local logging, request/response capture, and sandbox isolation
Isolating and Reproducing Failures
- Methods for constructing minimal failing examples and test seeds
- Stateful versus stateless interactions: pinpointing context-dependent bugs
- Managing determinism, randomness, and nondeterministic behaviors
Behavioral Assessment and Metrics
- Quantitative measures: accuracy, ROUGE/BLEU variants, calibration, and perplexity approximations
- Qualitative assessment: human-in-the-loop scoring and rubric development
- Task-specific accuracy validation and acceptance criteria
Regression Testing and Automation
- Unit tests for prompts and components, alongside scenario-based and end-to-end testing
- Building regression suites and establishing baselines with golden examples
- Integrating Ollama model updates and automated validation gates into CI/CD
Monitoring and Observability
- Implementation of structured logging, distributed tracing, and correlation IDs
- Essential operational metrics: latency, token consumption, error rates, and quality indicators
- Configuration of alerts, dashboards, and SLIs/SLOs for model-driven services
Advanced Root Cause Investigation
- Tracing through prompt graphs, tool invocations, and multi-turn interactions
- Comparative A/B analysis and ablation studies
- Data lineage, dataset debugging, and mitigating dataset-driven failures
Remediation, Robustness, and Safety
- Mitigation strategies: filtering, grounding, retrieval augmentation, and prompt structuring
- Model update patterns: rollback, canary releases, and phased rollouts
- Post-incident reviews, lessons learned, and continuous improvement cycles
Summary and Path Forward
Requirements
- Extensive experience in developing and deploying LLM applications
- Proficiency with Ollama workflows and model hosting capabilities
- Working knowledge of Python, Docker, and foundational observability tools
Target Audience
- AI Engineers
- MLOps Professionals
- QA teams managing production-grade LLM systems
