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Duration 35 hours
Course Outline
Introduction to Diagnostic Foundations
- An overview of failure modes in LLM systems and specific Ollama-related issues
- Establishing reproducible experiments within controlled environments
- The debugging toolkit: local logs, request/response capture, and sandboxing
Reproducing and Isolating Failures
- Methods for generating minimal failing examples and seeds
- Distinguishing stateful vs. stateless interactions to isolate context-related bugs
- Managing determinism, randomness, and controlling non-deterministic behavior
Behavioral Evaluation and Metrics
- Quantitative metrics: accuracy, ROUGE/BLEU variants, calibration, and perplexity proxies
- Qualitative assessments: human-in-the-loop scoring and rubric development
- Task-specific fidelity checks and definition of acceptance criteria
Automated Testing and Regression
- Unit testing for prompts and components, along with scenario and end-to-end tests
- Building regression suites and establishing golden example baselines
- Integrating Ollama model updates and automated validation gates into CI/CD
Observability and Monitoring
- Implementing structured logging, distributed tracing, and correlation IDs
- Tracking 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 flows
- Conducting comparative A/B diagnosis and ablation studies
- Analyzing data provenance, dataset debugging, and resolving dataset-induced failures
Safety, Robustness, and Remediation Strategies
- Implementing 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 Future Steps
Requirements
- Substantial experience in building and deploying LLM applications
- Proficiency with Ollama workflows and model hosting processes
- Working knowledge of Python, Docker, and fundamental observability tools
Target Audience
- AI Engineers
- MLOps Professionals
- QA Teams overseeing production LLM systems
