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Duration 35 hours
Course Outline
Introduction and Diagnostic Foundations
- Exploration of common LLM failure modes and Ollama-specific challenges.
- Establishing reproducible experiments and controlled testing environments.
- Utilizing the debugging toolkit: local logs, request/response analysis, and sandboxing techniques.
Reproducing and Isolating Failures
- Strategies for creating minimal failing examples and seeds.
- Distinguishing between stateful and stateless interactions to isolate context-related bugs.
- Managing determinism, randomness, and controlling nondeterministic 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
- Writing unit tests 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 pipelines.
Observability and Monitoring
- Implementing structured logging, distributed traces, and correlation IDs.
- Tracking key operational metrics: latency, token usage, error rates, and quality signals.
- Setting up alerting, dashboards, and SLIs/SLOs for model-backed services.
Advanced Root Cause Analysis
- Tracing through graphed prompts, tool calls, and multi-turn flows.
- Performing comparative A/B diagnosis and ablation studies.
- Investigating data provenance, dataset debugging, and resolving dataset-induced failures.
Safety, Robustness, and Remediation Strategies
- Applying mitigations such as filtering, grounding, retrieval augmentation, and prompt scaffolding.
- Utilizing rollback, canary, and phased rollout patterns for model updates.
- Conducting post-mortems, analyzing lessons learned, and establishing continuous improvement loops.
Summary and Next Steps
Requirements
- Extensive experience in developing and deploying LLM applications.
- Proficiency with Ollama workflows and model hosting practices.
- Strong familiarity with Python, Docker, and foundational observability tools.
Target Audience
- AI Engineers.
- MLOps Professionals.
- QA Teams overseeing production LLM systems.
