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Duration 35 hours
Course Outline
Introduction and Diagnostic Foundations
- Survey of LLM failure modes and prevalent Ollama-specific issues.
- Methods for establishing reproducible experiments and controlled environments.
- Debugging toolkit: local logs, request/response capture, and sandboxing techniques.
Reproducing and Isolating Failures
- Strategies for creating minimal failing examples and seeds.
- Distinguishing stateful from stateless interactions to isolate context-related bugs.
- Managing determinism, randomness, and nondeterministic behaviors.
Behavioral Evaluation and Metrics
- Quantitative measures: accuracy, ROUGE/BLEU variants, calibration, and perplexity proxies.
- Qualitative assessments: human-in-the-loop scoring and rubric construction.
- Task-specific fidelity verification and acceptance criteria.
Automated Testing and Regression
- Unit tests for prompts and components, along with scenario and end-to-end testing.
- Development of regression suites and golden example baselines.
- CI/CD integration for Ollama model updates and automated validation gates.
Observability and Monitoring
- Structured logging, distributed tracing, and correlation ID management.
- Critical operational metrics: latency, token consumption, error rates, and quality indicators.
- Alerting, dashboards, and SLIs/SLOs for model-backed services.
Advanced Root Cause Analysis
- Tracing complex paths through graphed prompts, tool calls, and multi-turn flows.
- Conducting comparative A/B diagnoses and ablation studies.
- Data provenance, dataset debugging, and resolving dataset-induced failures.
Safety, Robustness, and Remediation Strategies
- Mitigation techniques: filtering, grounding, retrieval augmentation, and prompt scaffolding.
- Implementation of rollback, canary, and phased rollout patterns for model updates.
- Conducting post-mortems, documenting lessons learned, and fostering continuous improvement.
Summary and Next Steps
Requirements
- Extensive experience in developing and deploying LLM applications.
- Proficiency with Ollama workflows and model hosting practices.
- Working knowledge of Python, Docker, and foundational observability tools.
Target Audience
- AI Engineers
- ML Ops Specialists
- QA Teams managing production LLM systems
