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Duration 35 hours
Course Outline
Introduction and Diagnostic Foundations
- An overview of failure patterns in LLM systems and common Ollama-specific challenges
- Setting up reproducible experiments and controlled testing environments
- The debugging toolkit: local logs, request/response captures, and sandboxing techniques
Reproducing and Isolating Failures
- Methods for crafting minimal failing examples and seed inputs
- Distinguishing stateful from stateless interactions to isolate context-related bugs
- Managing determinism, randomness, and nondeterministic behaviors
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 verification and acceptance criteria
Automated Testing and Regression
- Unit testing for prompts and components, alongside scenario and end-to-end testing
- Constructing regression suites and baseline golden examples
- CI/CD integration for Ollama model updates and automated validation gates
Observability and Monitoring
- Structured logging, distributed tracing, and correlation ID usage
- Core operational metrics: latency, token consumption, error rates, and quality indicators
- Alerting mechanisms, dashboards, and SLIs/SLOs for model-backed services
Advanced Root Cause Analysis
- Tracing flows through graphed prompts, tool calls, and multi-turn conversations
- A/B comparative diagnosis and ablation studies
- Data provenance tracking, dataset debugging, and resolving dataset-induced failures
Safety, Robustness, and Remediation Strategies
- Mitigation techniques: filtering, grounding, retrieval augmentation, and prompt scaffolding
- Rollback, canary, and phased rollout patterns for model updates
- Post-mortems, lessons learned, and continuous improvement cycles
Summary and Next Steps
Requirements
- Significant experience in developing and deploying LLM applications
- Proficiency with Ollama workflows and model hosting mechanisms
- Confidence in using Python, Docker, and fundamental observability tools
Target Audience
- AI Engineers
- MLOps Specialists
- QA teams managing production LLM systems
