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Course Outline
Introduction and Diagnostic Foundations
- Overview of failure modes in LLM systems and common issues specific to Ollama for government use
- Establishing reproducible experiments and controlled environments for government applications
- Debugging toolset: local logs, request/response captures, and sandboxing techniques for government systems
Reproducing and Isolating Failures
- Techniques for creating minimal failing examples and seeds in a government context
- Stateful vs stateless interactions: isolating context-related bugs in government workflows
- Determinism, randomness, and controlling nondeterministic behavior in government systems
Behavioral Evaluation and Metrics
- Quantitative metrics: accuracy, ROUGE/BLEU variants, calibration, and perplexity proxies for government applications
- Qualitative evaluations: human-in-the-loop scoring and rubric design for government use cases
- Task-specific fidelity checks and acceptance criteria for government projects
Automated Testing and Regression
- Unit tests for prompts and components, scenario and end-to-end tests for government systems
- Creating regression suites and golden example baselines for government applications
- CI/CD integration for Ollama model updates and automated validation gates in government workflows
Observability and Monitoring
- Structured logging, distributed traces, and correlation IDs for government systems
- Key operational metrics: latency, token usage, error rates, and quality signals for government applications
- Alerting, dashboards, and SLIs/SLOs for model-backed services in a government context
Advanced Root Cause Analysis
- Tracing through graphed prompts, tool calls, and multi-turn flows in government systems
- Comparative A/B diagnosis and ablation studies for government applications
- Data provenance, dataset debugging, and addressing dataset-induced failures in government workflows
Safety, Robustness, and Remediation Strategies
- Mitigations: filtering, grounding, retrieval augmentation, and prompt scaffolding for government systems
- Rollback, canary, and phased rollout patterns for model updates in government applications
- Post-mortems, lessons learned, and continuous improvement loops for government projects
Summary and Next Steps
Requirements
- Proven expertise in developing and deploying large language model (LLM) applications for government
- Familiarity with Ollama workflows and model hosting environments
- Proficiency with Python, Docker, and basic observability tools
Audience
- Artificial Intelligence (AI) engineers
- Machine Learning Operations (ML Ops) professionals
- Quality Assurance (QA) teams responsible for production LLM systems
35 Hours
