QA and Software Testing + AI: course structure and content

Course overview

Description generated based on course syllabus and open data.

The program blends QA (quality assurance), Software Testing, and AI methods: from SDLC and test design techniques to SQL, documentation, and LLM usage to speed up checks and artifact preparation.

QA and Software Testing + AI: program at a glance

• IT project basics: SDLC, roles, artifacts, Scrum/Kanban.
• Manual QA: testing types, test design techniques, reporting, defect management.
• AI Skills: LLM, prompting for tests and docs, basic API integration.
• Databases and SQL: data models, SELECT/WHERE/JOIN/AGG, data validation.
• Tools: Jira/YouTrack, TestRail/Sheets, Postman, basic API requests.

Modules and tools in QA, Software Testing and AI

• Documentation: checklists, test cases, bug reports, concise test plans.
• Practice: web app tasks, SQL queries, AI-driven workflows.
• Online format: live Zoom sessions, recordings for review.

Who this QA course with Software Testing and AI fits / who it may not fit

Suitable for

• Beginners with no prior experience who need structured Manual QA knowledge.
• Adjacent roles (Support, BA, PM) requiring Software Testing essentials.
• Those aiming to apply AI (LLM) to optimize quality assurance routines.

Not suitable for

• Those seeking only automation without Manual QA and SQL basics.
• Those unwilling to do regular homework and maintain documentation.
• Those expecting instant outcomes without practice.

Problem → learning effect in QA and AI

• Unclear testing approaches → core test design techniques and structured test cases.
• Difficult reporting and bug descriptions → practical templates for checklists and bug reports.
• Slow material preparation → prompting in LLM for drafts of tests and notes.
• Data validation mistakes → SQL queries to verify DB state.
• Scattered toolset → aligned stack: Jira/YouTrack, Postman, spreadsheets/testrail-like flow.

Comparison with Software Testing alternatives

Self-study

Flexible, but time-consuming curation of sources and quality control of materials.

Video-only courses

Good for topic overview, yet limited feedback and fewer hands-on artifacts.

Academic programs

Strong theory, longer timelines, often less focus on practical QA tools.

Live online sessions in QA and AI

Regular mentor interaction, practical tasks, recordings for repetition.

Expected learning outcomes in Manual QA, SQL and AI

• Understanding of SDLC, roles, and quality assurance processes.
• Ability to produce basic test cases, checklists, bug reports, and a concise test plan.
• Application of SQL for data validation and business logic checks.
• Use of AI (LLM) to generate drafts of tests and documentation with subsequent editing.
• Basic operation of Jira/YouTrack, Postman, and API requests.