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Duration 35 hours
Course Outline
Introduction and Diagnostic Foundations
- Analysis of common failure modes in LLM systems and Ollama-specific issues
- Establishing reproducible experiments and controlled testing environments
- Debugging toolkit: local logs, request/response captures, and sandboxing techniques
Reproducing and Isolating Failures
- Methods for creating minimal failing examples and test seeds
- Distinguishing stateful versus stateless interactions to isolate context-related bugs
- Managing determinism, randomness, and controlling nondeterministic behavior
Behavioral Evaluation and Metrics
- Quantitative metrics: accuracy, ROUGE/BLEU variants, calibration, and perplexity proxies
- Qualitative assessments: human-in-the-loop scoring and rubric design
- Task-specific fidelity checks and defining acceptance criteria
Automated Testing and Regression
- Unit testing for prompts and components, alongside scenario and end-to-end tests
- Developing regression suites and golden example baselines
- Integrating Ollama model updates into CI/CD with automated validation gates
Observability and Monitoring
- Structured logging, distributed tracing, and correlation ID implementation
- Key operational metrics: latency, token usage, error rates, and quality signals
- Configuring alerting, dashboards, and SLIs/SLOs for model-backed services
Advanced Root Cause Analysis
- Tracing through graphed prompts, tool calls, and multi-turn conversation flows
- Conducting comparative A/B diagnosis and ablation studies
- Investigating data provenance, dataset debugging, and mitigating dataset-induced failures
Safety, Robustness, and Remediation Strategies
- Implementation of mitigations: filtering, grounding, retrieval augmentation, and prompt scaffolding
- Applying rollback, canary, and phased rollout patterns for model updates
- Conducting post-mortems, documenting lessons learned, and establishing continuous improvement loops
Summary and Next Steps
Requirements
- Extensive experience in building and deploying LLM applications
- Proficiency with Ollama workflows and model hosting protocols
- Working knowledge of Python, Docker, and fundamental observability tools
Target Audience
- AI Engineers
- ML Ops Professionals
- QA Teams managing production-grade LLM systems
