Mastering AI-Driven Problem Solving in Engineering with Observability

Stop guessing during outages. Get a step-by-step blueprint for debugging complex systems, from microservice failures to AI model hallucinations.

#1about 2 minutes

Understanding observability and the need for a process

Observability provides insight into system health and performance, addressing the common lack of a methodical process for resolving issues in complex environments.

#2about 2 minutes

Navigating the complexity of highly distributed systems

A real-world example of a distributed trace highlights the challenges of debugging systems with thousands of microservices, databases, and daily deployments.

#3about 4 minutes

Understanding the four core telemetry data types

Effective problem-solving requires leveraging the distinct strengths of metrics, events, logs, and distributed traces to gain a complete picture of system behavior.

#4about 5 minutes

Key data sources and platform capabilities for observability

A comprehensive observability strategy involves monitoring all application layers and utilizing platform features like workloads, change tracking, and AI-driven intelligence.

#5about 1 minute

Prioritizing changes and errors for faster resolution

Insights from a Microsoft Azure study reveal that most production issues stem from software faults or bad data, making rollbacks a common and effective first solution.

#6about 6 minutes

A step-by-step framework for debugging complex systems

Follow a structured process for incident resolution by first checking for changes and errors, then examining local and remote dependencies before using traces to investigate further.

#7about 3 minutes

Strategies for mitigating AI model hallucinations

Combat AI hallucinations by constraining model inputs and outputs, providing additional context through retrieval-augmented generation (RAG), and eventually fine-tuning the model.

#8about 3 minutes

Deciding when to build versus buy LLM solutions

Evaluate the trade-offs between using consumption-based AI tools and building smaller, custom LLMs based on factors like request volume, cost, and data privacy.