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Duration 35 hours
Course Outline
Introduction and Diagnostic Foundations
- Overview of failure modes in LLM systems and common Ollama-specific challenges
- Establishing reproducible experiments and controlled environments
- Debugging toolkit: local logs, request/response captures, and sandboxing techniques
Reproducing and Isolating Failures
- Techniques for crafting minimal failing examples and test seeds
- Stateful versus stateless interactions: isolating context-dependent bugs
- Managing determinism, randomness, and controlling nondeterministic behavior
Behavioral Evaluation and Metrics
- Quantitative metrics: accuracy, ROUGE/BLEU variants, calibration, and perplexity proxies
- Qualitative evaluations: human-in-the-loop scoring and rubric design
- Task-specific fidelity checks and defining acceptance criteria
Automated Testing and Regression
- Unit tests for prompts and components, along with scenario and end-to-end tests
- Building regression suites and establishing golden example baselines
- CI/CD integration for Ollama model updates and automated validation gates
Observability and Monitoring
- Structured logging, distributed traces, and correlation IDs
- Key operational metrics: latency, token usage, error rates, and quality signals
- Alerting systems, dashboards, and SLIs/SLOs for model-backed services
Advanced Root Cause Analysis
- Tracing through graphed prompts, tool calls, and multi-turn flows
- Comparative A/B diagnosis and ablation studies
- Data provenance, dataset debugging, and mitigating dataset-induced failures
Safety, Robustness, and Remediation Strategies
- Mitigation strategies: filtering, grounding, retrieval augmentation, and prompt scaffolding
- Rollback, canary, and phased rollout patterns for model updates
- Post-mortems, lessons learned, and continuous improvement loops
Summary and Next Steps
Requirements
- Extensive experience in building and deploying LLM applications
- Proficiency with Ollama workflows and model hosting environments
- Working knowledge of Python, Docker, and foundational observability tools
Intended Audience
- AI engineers
- ML Ops professionals
- QA teams overseeing production LLM systems
