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Duration 35 hours
Course Outline
Introduction and Diagnostic Foundations
- Surveying failure modes in LLM systems and identifying common Ollama-specific challenges.
- Setting up reproducible experiments and controlled testing environments.
- Mastering the debugging toolkit: local logs, request/response capture, and sandboxing.
Reproducing and Isolating Failures
- Techniques for generating minimal failing examples and effective seeds.
- Distinguishing between stateful and stateless interactions to isolate context-dependent bugs.
- Managing determinism, randomness, and controlling non-deterministic behaviors.
Behavioral Evaluation and Metrics
- Applying quantitative metrics such as accuracy, ROUGE/BLEU variants, calibration, and perplexity proxies.
- Conducting qualitative evaluations through human-in-the-loop scoring and rubric design.
- Defining task-specific fidelity checks and acceptance criteria.
Automated Testing and Regression
- Implementing unit tests for prompts and components, alongside scenario and end-to-end tests.
- Building regression suites and establishing golden example baselines.
- Integrating Ollama model updates into CI/CD pipelines with automated validation gates.
Observability and Monitoring
- Utilizing structured logging, distributed tracing, and correlation IDs.
- Tracking key operational metrics including latency, token usage, error rates, and quality signals.
- Configuring alerting, dashboards, and SLIs/SLOs for model-backed services.
Advanced Root Cause Analysis
- Tracing issues through graphed prompts, tool calls, and multi-turn flows.
- Performing comparative A/B diagnosis and ablation studies.
- Analyzing data provenance, debugging datasets, and addressing dataset-induced failures.
Safety, Robustness, and Remediation Strategies
- Applying mitigations such as filtering, grounding, retrieval augmentation, and prompt scaffolding.
- Employing rollback, canary, and phased rollout patterns for model updates.
- Conducting post-mortems, extracting lessons learned, and establishing continuous improvement loops.
Summary and Next Steps
Requirements
- Substantial experience in developing and deploying LLM applications.
- Working familiarity with Ollama workflows and model hosting processes.
- Proficiency in Python, Docker, and fundamental observability tooling.
Audience
- AI Engineers
- ML Ops Professionals
- QA Teams managing production LLM systems.
