A structured collection of real-world support engineering workflows, investigation patterns, and operational playbooks used to troubleshoot API issues, diagnose system behavior, and manage production incidents.
This repository is designed to demonstrate practical support engineering thinking: how to move from vague customer reports to clear technical hypotheses, targeted investigation steps, and actionable resolutions.
Modern support engineering sits at the intersection of systems thinking, debugging, and customer communication.
This toolkit documents:
- How to triage and structure incoming support requests
- How to investigate API and system-level issues efficiently
- How to use SQL for behavioral validation and data verification
- How to respond during incidents with clarity and speed
- How to escalate issues with complete technical context
- How to apply AI-assisted workflows to reduce investigation time
The goal is not theory. It’s repeatable execution patterns.
Defines how incoming tickets are interpreted, normalized, and prioritized.
Focus areas:
- Converting ambiguous customer reports into structured problem statements
- Identifying severity and impact signals
- Capturing missing context early
- Separating user error from system behavior
Core API debugging workflows.
Focus areas:
- Request/response lifecycle analysis
- Authentication and authorization issues
- Latency and timeout investigation
- Payload validation and schema mismatches
- Correlating API behavior with logs and system signals
Data-driven debugging using SQL queries.
Focus areas:
- Validating system state against expected outcomes
- Investigating inconsistencies in backend data
- Tracing user actions through relational data models
- Confirming whether issues are data-related or system-related
Operational response during production issues.
Focus areas:
- Incident classification and severity levels
- Triage under pressure
- Communication templates for stakeholders
- Timeline reconstruction during outages
- Post-incident stabilization steps
How to escalate issues effectively to engineering teams.
Focus areas:
- Structuring escalation reports
- Including reproducible steps and logs
- Reducing back-and-forth with engineering
- Defining clear “what we know vs unknowns”
- Preventing incomplete escalations
How LLMs and automation tools improve support engineering efficiency.
Focus areas:
- Converting raw tickets into structured diagnostics
- Summarizing logs and error patterns
- Suggesting likely root causes
- Drafting investigation plans
- Reducing time-to-resolution through assisted reasoning
This toolkit is intended to be used as:
- A personal reference during investigations
- A training resource for support engineers
- A portfolio demonstration of structured troubleshooting thinking
- A foundation for scaling support workflows with AI
Each folder represents a stage of real-world support work, not isolated theory.
Good support engineering is not about memorizing errors.
It’s about:
- Reducing ambiguity quickly
- Asking the right next question
- Building a mental model of the system
- Proving or disproving hypotheses fast
- Communicating clearly under uncertainty
This repository reflects that workflow-first mindset.