Advanced Ollama Model Debugging & Evaluation Training Course

Introduction and Diagnostic Foundations

• Overview of failure modes in language learning models (LLMs) and common issues specific to Ollama systems for government applications
• Establishing reproducible experiments and controlled environments to ensure consistent results for government operations
• Debugging toolset: local logs, request/response captures, and sandboxing techniques to facilitate effective troubleshooting for government use cases

Reproducing and Isolating Failures

• Techniques for creating minimal failing examples and seeds to identify specific issues in government applications
• Stateful vs stateless interactions: isolating context-related bugs to ensure reliable performance for government services
• Determinism, randomness, and controlling nondeterministic behavior to maintain consistency in government operations

Behavioral Evaluation and Metrics

• Quantitative metrics: accuracy, ROUGE/BLEU variants, calibration, and perplexity proxies for assessing model performance in government contexts
• Qualitative evaluations: human-in-the-loop scoring and rubric design to ensure alignment with government standards and requirements
• Task-specific fidelity checks and acceptance criteria to validate the effectiveness of models for government tasks

Automated Testing and Regression

• Unit tests for prompts and components, scenario and end-to-end tests to ensure robustness in government applications
• Creating regression suites and golden example baselines to maintain consistency over time for government use
• CI/CD integration for Ollama model updates and automated validation gates to support continuous improvement in government systems

Observability and Monitoring

• Structured logging, distributed traces, and correlation IDs to enhance transparency and traceability in government operations
• Key operational metrics: latency, token usage, error rates, and quality signals to monitor performance for government services
• Alerting, dashboards, and SLIs/SLOs (Service Level Indicators/Service Level Objectives) for model-backed services to ensure reliability in government applications

Advanced Root Cause Analysis

• Tracing through graphed prompts, tool calls, and multi-turn flows to identify complex issues in government systems
• Comparative A/B diagnosis and ablation studies to understand model behavior for government use cases
• Data provenance, dataset debugging, and addressing dataset-induced failures to improve accuracy in government applications

Safety, Robustness, and Remediation Strategies

• Mitigations: filtering, grounding, retrieval augmentation, and prompt scaffolding to enhance security and reliability for government operations
• Rollback, canary, and phased rollout patterns for model updates to minimize disruptions in government services
• Post-mortems, lessons learned, and continuous improvement loops to foster ongoing enhancement of government systems

Summary and Next Steps