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Course Outline
Introduction and Diagnostic Foundations
- Overview of failure modes in LLM systems and common Ollama-specific issues for government deployments
- Establishing reproducible experiments and controlled environments
- Debugging toolset: local logs, request/response captures, and sandboxing
Reproducing and Isolating Failures
- Techniques for creating minimal failing examples and seeds
- Stateful vs stateless interactions: isolating context-related bugs
- 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 acceptance criteria for government use cases
Automated Testing and Regression
- Unit tests for prompts and components, scenario and end-to-end tests
- Creating regression suites and 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, dashboards, and SLIs/SLOs for model-backed services supporting government operations
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 addressing dataset-induced failures
Safety, Robustness, and Remediation Strategies
- Mitigations: filtering, grounding, retrieval augmentation, and prompt scaffolding
- Rollback, canary, and phased rollout patterns for model updates in government environments
- Post-mortems, lessons learned, and continuous improvement loops
Summary and Next Steps
Requirements
- Demonstrated proficiency in the development and deployment of large language model solutions.
- Knowledge of Ollama operational workflows and model serving infrastructure.
- Competency in Python, Docker containerization, and foundational observability systems.
Audience
- Artificial Intelligence engineers.
- Machine Learning Operations (MLOps) specialists.
- Quality Assurance personnel managing production LLM systems for government applications.
35 Hours
