Break Into AI Testing: The Next-Gen Quality Engineer Skillset
Break Into AI Testing; The Next-Gen Quality Engineer Skillset
Become an AI & LLM Testing Expert in 5 Weeks with the Only Live Program That Trains Manual QAs to Future-proof Their QA Careers.
Next cohort: 17th Jan
Live , Zoom
Become an AI & LLM Testing expert in 5 weeks. Hands-on, project-based training that future-proofs your QA career.
Next Cohort Starts: Jan 17
Live, Online, Zoom
Work on a real US-based AI startup project you can add to your resume.
Secure Your Career. Only hands-on, project-based training in the world.
Go from manual QA to next-gen AI QA Leader!
Work on a real US-based AI startup project that you can add to your resume.
Secure Your Career. Only hands-on, project-based training in the world. Go from manual QA to next-gen AI QA Leader!
Work on a real US-based AI startup project you can add to your resume.
Secure Your Career. Only hands-on, project-based training in the world.
Go from manual QA to next-gen AI QA Leader!
20 seats only - 2 expert instructors
20 seats only - 2 expert instructors
20 seats only - 2 expert instructors
40 Live Hours + 80hrs hands-on exercises (5% theory, 95% practical)
40 Live Hours + 80 hrs hands-on exercises (5% theory, 95% practical)
40 Live Hours + 80hrs hands-on exercises (5% theory, 95% practical)
5 Weeks Long - Based on a US startup project you can add in your CV
5 Weeks Long - Based on a US startup project you can add in your CV
5 Weeks Long - Based on a US startup project you can add in your CV
Schedule: Weekend classes (Sat & Sun) 1.00 PM - 5.00 PM CEST
Schedule: Weekend classes (Sat & Sun) 1.00 PM - 5.00 PM CEST
Schedule: Weekend classes (Sat & Sun) 1.00 PM - 5.00 PM CEST
Original Value: $2,499
For You:
$1,250
100% Money-Back Guarantee
100% Money-Back Guarantee
How Testers Like You Are Transforming Their Careers with This Program?
How Testers Like You Are Transforming Their Careers with This Program?
How Testers Like You Are Transforming Their Careers with This Program?
"I’d absolutely recommend it"
Hershal Walton
Product Manager
"I left buzzing with so many ideas"
Max Volvich
QA Leader
"I’d absolutely recommend it"
Hershal Walton
Product Manager
"I left buzzing with so many ideas"
Max Volvich
QA Leader
"I’d absolutely recommend it"
Hershal Walton
Product Manager, TIAA
"I left buzzing with so many ideas"
Max Volvich
QA Manager
What Makes This Program Truly Different from Other Online Programs
What Makes This Program Truly Different from Other Online Programs
What Makes This Program Truly Different from Other Online Programs
We are the only project-based training that teaches QA professionals with hands-on experience.
We focus on building QA careers so week 5 is all about career positioning with hands-on team exercises.
You’ll work on a real US based AI startup which you can add on your resume.
All live sessions are recorded and can be accessed up to 1 year.
Is this program for you?
Is this program for you?
Is this program for you?
You're a manual QA with 3+ years of experience.
You're a manual QA with 3+ years of experience.
You don't need coding or AI experience.
You fear being replaced and want to secure your QA career to become the next-gen AI QA leader.
Requirements
Why learning AI & LLM Testing is a must for Every QA in 2025?
You must have 3+ years of manual QA experience
No coding or programming experience needed
Laptop with minimum specifications
Laptop with minimum specifications
Why learning AI & LLM Testing is a must for Every QA in 2025-2026?
Why learning AI & LLM Testing is a must for Every QA in 2025-2026?
Why learning AI & LLM Testing is a must for Every QA in 2025-2026?
AI Skills in QA have tripled in past years
More than 3,000+ job openings across the US
Top AI QAs earn over $300K/year
AI Testing Skills are in-demand in every company
Almost every company is building their AI products
The future of QA isn't about choosing between Selenium or Playwright - it's about Mastering Prompt Engineering, LLM Testing and AI Debugging.
The future of QA isn't about choosing between Selenium or Playwright - it's about Mastering Prompt Engineering, LLM Testing and AI Debugging.
The future of QA isn't about choosing between Selenium or Playwright - it's about Mastering Prompt Engineering, LLM Testing and AI Debugging.
100% Money-Back Guarantee
100% money-back guarantee
100% money-back guarantee
AI Skills in QA have tripled in past years
Top AI QAs earn over $300K/ year
More than 3000+ job openings for AI testers across the US
AI Testing Skills are in-demand in every company
Almost every company is building their AI products
What you'll achieve in 5-weeks
Week 1 (Foundation & Tool Mastery)
Day 1: AI Fundamentals & Setup
Introduction: Course rules, setting up the permanent Discord community channel. Theory: LLM basics, transformer architecture, differences between traditional software testing and AI system testing. Hands-on: Environment setup (local and cloud models). Initial interactive model exploration comparing local (LM Studio) and commercial (OpenAI, Anthropic) models using the same prompt. Vulnerabilities: Introduction to the seven unique AI testing challenges (e.g., security, hallucination, bias). Homework: Complete environment setup and design 3+ test prompts targeting the vulnerabilities.
Day 2: PromptFoo Basics
Tool Introduction: Learning PromptFoo for systematic AI testing. Shift to hands-on, Q&A, and group exercises (minimal presentations). Configuration: Overview of PromptFoo's configuration (YAML structure), including providers (LLMs), prompts, and initial deterministic assertions (pass/fail checks). Practice utilizing variables within prompts. Integration: Testing commercial and local models via PromptFoo. Homework: Practice building test suites and reviewing assertions documentation.
Week 2 (Foundation & Tool Mastery)
Day 3: Prompt Engineering and Cost Evaluation
Prompt Engineering: Defining the rules and constraints of the system (System Prompt) and crafting effective test inputs (User Prompt). Using AI (LLMs) to generate effective test prompts. Evaluation: Hands-on workshop on LLM cost evaluation (budgeting) by running prompts against multiple models to compare cost per request. Organization: Structuring the testing framework using file-based prompt configurations.
Day 4: Advanced Assertion & Career Prep
Advanced Testing: Deep dive into assertions, particularly Model-Graded Assertions (MGA), where an LLM acts as a judge (LLM Rubric) to evaluate output quality (relevancy, factuality). Testing using CSV-based files for structured test data. Career Start: Introduction to LinkedIn Personal Branding; documenting early achievements and incorporating AI testing keywords (e.g., prompt engineering, LLM testing) to profiles. Homework: Review PromptFoo Red Teaming documentation.
Week 3 (Application Testing & Advanced Techniques
Day 5: Red Teaming Concepts
Equator Point: Course halfway review. Red Teaming: Defining red teaming as simulating adversarial inputs (like a comprehensive baseline report) to find vulnerabilities (e.g., security, bias). Discussion of vulnerability frameworks like the OWASP Top 10 for AI. Strategy: Understanding Red Teaming workflow (defining strategy, execution, analysis) and configurations. Comparison of red teaming types (Small, Large, XXL/Extensive). Homework: Prepare application details required for WeOptimize red teaming configuration (using AI assistance).
Day 6: Testing a Real Application (WeOptimize)
Application Architecture: Reviewing the high-level architecture of the WeOptimize application (Orchestrator, Guard LLM, specialized LLMs, knowledge bases like Jira/Confluence/Figma). Testing Mode: Focusing on end-to-end black box exploratory testing via the application's chat interface. Using the provided "Source of Truth" as acceptance criteria. Bug Reporting: Hands-on exercise reporting and documenting bugs in ClickUp, including reproduction steps and linking them to specific AI vulnerabilities.
Week 4 (Application Testing & Advanced Techniques
Day 7: Red Teaming Execution & Triage
Application Setup: Finalizing the Red Teaming configuration by inputting a comprehensive application context (main purpose, features, system rules) into PromptFoo. Group Triage: Teams exchange reported bugs and attempt to reproduce and validate issues found by classmates. Advanced Testing: Hands-on session applying PromptFoo for complex scenarios, including multi-turn conversation testing using JSON objects for regression. Tool Exposure: Alternative testing tool.
Day 8: Tool Exploration & Post-Launch Monitoring
Post-Launch Tools: Demo and discussion of tools used for monitoring and maintaining LLM models pre- and post-launch (e.g., ATARO wrapper). New Tool Assignment: Introduction to Agenta (a comparable, alternative LLM testing platform). Homework: Explore and evaluate Agenta to apply foundational testing concepts learned from PromptFoo. Research other AI testing tools in the market (consulting mindset).
Week 5 (Career Acceleration)
Day 9: Agenta Review & LinkedIn Strategy
Tool Comparison: Reviewing homework findings on Agenta, applying foundational concepts (Evals, variables) learned from PromptFoo to a new platform. Career Branding: Strategies for content creation and influence building on LinkedIn. Using generative AI tools (e.g., Claude, ChatGPT) as brainstorming partners for posts, while avoiding generic copy-paste content. Accomplishments: Workshop focused on drafting AI LLM testing accomplishment statements for resumes/profiles, quantifying the business impact of skills learned and bugs found in WeOptimize. Homework: Post tailored AI testing content on LinkedIn and engage (comment/repost) with classmates' posts.
Day 10: Final Optimization & Interview Prep
Final Profile Optimization: Updating LinkedIn profiles and resumes with core AI testing skills (prompt engineering, red teaming, hallucination detection, token consumption). Interview Preparation: Review of common AI testing interview questions (e.g., scaling tests, verifying factual responses, token consumption, security, testing LLMs with other LLMs). Wrap-up: Final remarks, community engagement commitment, and discussion of post-course resources.
What you'll achieve in 5-weeks
Week 1 (Foundation & Tool Mastery)
Day 1: AI Fundamentals & Setup
Introduction: Course rules, setting up the permanent Discord community channel. Theory: LLM basics, transformer architecture, differences between traditional software testing and AI system testing. Hands-on: Environment setup (local and cloud models). Initial interactive model exploration comparing local (LM Studio) and commercial (OpenAI, Anthropic) models using the same prompt. Vulnerabilities: Introduction to the seven unique AI testing challenges (e.g., security, hallucination, bias). Homework: Complete environment setup and design 3+ test prompts targeting the vulnerabilities.
Day 2: PromptFoo Basics
Tool Introduction: Learning PromptFoo for systematic AI testing. Shift to hands-on, Q&A, and group exercises (minimal presentations). Configuration: Overview of PromptFoo's configuration (YAML structure), including providers (LLMs), prompts, and initial deterministic assertions (pass/fail checks). Practice utilizing variables within prompts. Integration: Testing commercial and local models via PromptFoo. Homework: Practice building test suites and reviewing assertions documentation.
Week 2 (Foundation & Tool Mastery)
Day 3: Prompt Engineering and Cost Evaluation
Prompt Engineering: Defining the rules and constraints of the system (System Prompt) and crafting effective test inputs (User Prompt). Using AI (LLMs) to generate effective test prompts. Evaluation: Hands-on workshop on LLM cost evaluation (budgeting) by running prompts against multiple models to compare cost per request. Organization: Structuring the testing framework using file-based prompt configurations.
Day 4: Advanced Assertion & Career Prep
Advanced Testing: Deep dive into assertions, particularly Model-Graded Assertions (MGA), where an LLM acts as a judge (LLM Rubric) to evaluate output quality (relevancy, factuality). Testing using CSV-based files for structured test data. Career Start: Introduction to LinkedIn Personal Branding; documenting early achievements and incorporating AI testing keywords (e.g., prompt engineering, LLM testing) to profiles. Homework: Review PromptFoo Red Teaming documentation.
Week 3 (Application Testing & Advanced Techniques
Day 5: Red Teaming Concepts
Equator Point: Course halfway review. Red Teaming: Defining red teaming as simulating adversarial inputs (like a comprehensive baseline report) to find vulnerabilities (e.g., security, bias). Discussion of vulnerability frameworks like the OWASP Top 10 for AI. Strategy: Understanding Red Teaming workflow (defining strategy, execution, analysis) and configurations. Comparison of red teaming types (Small, Large, XXL/Extensive). Homework: Prepare application details required for WeOptimize red teaming configuration (using AI assistance).
Day 6: Testing a Real Application (WeOptimize)
Application Architecture: Reviewing the high-level architecture of the WeOptimize application (Orchestrator, Guard LLM, specialized LLMs, knowledge bases like Jira/Confluence/Figma). Testing Mode: Focusing on end-to-end black box exploratory testing via the application's chat interface. Using the provided "Source of Truth" as acceptance criteria. Bug Reporting: Hands-on exercise reporting and documenting bugs in ClickUp, including reproduction steps and linking them to specific AI vulnerabilities.
Week 4 (Application Testing & Advanced Techniques
Deep dive into Promptfoo.
Explore and integrate other open-source AI testing tools.
Debugging AI Failures based on test results.
Review/triage ClickUp Bugs
Advanced Promptfoo against Live AI Application
Tools: Promptfoo, Live AI Application, JSON files
Week 5 (Career Acceleration)
Day 9: Agenta Review & LinkedIn Strategy
Tool Comparison: Reviewing homework findings on Agenta, applying foundational concepts (Evals, variables) learned from PromptFoo to a new platform. Career Branding: Strategies for content creation and influence building on LinkedIn. Using generative AI tools (e.g., Claude, ChatGPT) as brainstorming partners for posts, while avoiding generic copy-paste content. Accomplishments: Workshop focused on drafting AI LLM testing accomplishment statements for resumes/profiles, quantifying the business impact of skills learned and bugs found in WeOptimize. Homework: Post tailored AI testing content on LinkedIn and engage (comment/repost) with classmates' posts.
Day 10: Final Optimization & Interview Prep
Final Profile Optimization: Updating LinkedIn profiles and resumes with core AI testing skills (prompt engineering, red teaming, hallucination detection, token consumption). Interview Preparation: Review of common AI testing interview questions (e.g., scaling tests, verifying factual responses, token consumption, security, testing LLMs with other LLMs). Wrap-up: Final remarks, community engagement commitment, and discussion of post-course resources.
What you'll achieve in 5-weeks
Week 1 (Foundation & Tool Mastery)
Day 1: AI Fundamentals & Setup
Introduction: Course rules, setting up the permanent Discord community channel. Theory: LLM basics, transformer architecture, differences between traditional software testing and AI system testing. Hands-on: Environment setup (local and cloud models). Initial interactive model exploration comparing local (LM Studio) and commercial (OpenAI, Anthropic) models using the same prompt. Vulnerabilities: Introduction to the seven unique AI testing challenges (e.g., security, hallucination, bias). Homework: Complete environment setup and design 3+ test prompts targeting the vulnerabilities.
Day 2: PromptFoo Basics
Tool Introduction: Learning PromptFoo for systematic AI testing. Shift to hands-on, Q&A, and group exercises (minimal presentations). Configuration: Overview of PromptFoo's configuration (YAML structure), including providers (LLMs), prompts, and initial deterministic assertions (pass/fail checks). Practice utilizing variables within prompts. Integration: Testing commercial and local models via PromptFoo. Homework: Practice building test suites and reviewing assertions documentation.
Week 2 (Foundation & Tool Mastery)
Day 3: Prompt Engineering and Cost Evaluation
Prompt Engineering: Defining the rules and constraints of the system (System Prompt) and crafting effective test inputs (User Prompt). Using AI (LLMs) to generate effective test prompts. Evaluation: Hands-on workshop on LLM cost evaluation (budgeting) by running prompts against multiple models to compare cost per request. Organization: Structuring the testing framework using file-based prompt configurations.
Day 4: Advanced Assertion & Career Prep
Advanced Testing: Deep dive into assertions, particularly Model-Graded Assertions (MGA), where an LLM acts as a judge (LLM Rubric) to evaluate output quality (relevancy, factuality). Testing using CSV-based files for structured test data. Career Start: Introduction to LinkedIn Personal Branding; documenting early achievements and incorporating AI testing keywords (e.g., prompt engineering, LLM testing) to profiles. Homework: Review PromptFoo Red Teaming documentation.
Week 3 (Application Testing & Advanced Techniques
Day 5: Red Teaming Concepts
Equator Point: Course halfway review. Red Teaming: Defining red teaming as simulating adversarial inputs (like a comprehensive baseline report) to find vulnerabilities (e.g., security, bias). Discussion of vulnerability frameworks like the OWASP Top 10 for AI. Strategy: Understanding Red Teaming workflow (defining strategy, execution, analysis) and configurations. Comparison of red teaming types (Small, Large, XXL/Extensive). Homework: Prepare application details required for WeOptimize red teaming configuration (using AI assistance).
Day 6: Testing a Real Application (WeOptimize)
Application Architecture: Reviewing the high-level architecture of the WeOptimize application (Orchestrator, Guard LLM, specialized LLMs, knowledge bases like Jira/Confluence/Figma). Testing Mode: Focusing on end-to-end black box exploratory testing via the application's chat interface. Using the provided "Source of Truth" as acceptance criteria. Bug Reporting: Hands-on exercise reporting and documenting bugs in ClickUp, including reproduction steps and linking them to specific AI vulnerabilities.
Week 4 (Application Testing & Advanced Techniques
Day 7: Red Teaming Execution & Triage
Application Setup: Finalizing the Red Teaming configuration by inputting a comprehensive application context (main purpose, features, system rules) into PromptFoo. Group Triage: Teams exchange reported bugs and attempt to reproduce and validate issues found by classmates. Advanced Testing: Hands-on session applying PromptFoo for complex scenarios, including multi-turn conversation testing using JSON objects for regression. Tool Exposure: Alternative testing tool.
Day 8: Tool Exploration & Post-Launch Monitoring
Post-Launch Tools: Demo and discussion of tools used for monitoring and maintaining LLM models pre- and post-launch (e.g., ATARO wrapper). New Tool Assignment: Introduction to Agenta (a comparable, alternative LLM testing platform). Homework: Explore and evaluate Agenta to apply foundational testing concepts learned from PromptFoo. Research other AI testing tools in the market (consulting mindset).
Week 5 (Career Acceleration)
Day 9: Agenta Review & LinkedIn Strategy
Tool Comparison: Reviewing homework findings on Agenta, applying foundational concepts (Evals, variables) learned from PromptFoo to a new platform. Career Branding: Strategies for content creation and influence building on LinkedIn. Using generative AI tools (e.g., Claude, ChatGPT) as brainstorming partners for posts, while avoiding generic copy-paste content. Accomplishments: Workshop focused on drafting AI LLM testing accomplishment statements for resumes/profiles, quantifying the business impact of skills learned and bugs found in WeOptimize. Homework: Post tailored AI testing content on LinkedIn and engage (comment/repost) with classmates' posts.
Day 10: Final Optimization & Interview Prep
Final Profile Optimization: Updating LinkedIn profiles and resumes with core AI testing skills (prompt engineering, red teaming, hallucination detection, token consumption). Interview Preparation: Review of common AI testing interview questions (e.g., scaling tests, verifying factual responses, token consumption, security, testing LLMs with other LLMs). Wrap-up: Final remarks, community engagement commitment, and discussion of post-course resources.
What you'll achieve in 5-weeks
Week 1 (Foundation & Tool Mastery)
Day 1: AI Fundamentals & Setup
Introduction: Course rules, setting up the permanent Discord community channel. Theory: LLM basics, transformer architecture, differences between traditional software testing and AI system testing. Hands-on: Environment setup (local and cloud models). Initial interactive model exploration comparing local (LM Studio) and commercial (OpenAI, Anthropic) models using the same prompt. Vulnerabilities: Introduction to the seven unique AI testing challenges (e.g., security, hallucination, bias). Homework: Complete environment setup and design 3+ test prompts targeting the vulnerabilities.
Day 2: PromptFoo Basics
Tool Introduction: Learning PromptFoo for systematic AI testing. Shift to hands-on, Q&A, and group exercises (minimal presentations). Configuration: Overview of PromptFoo's configuration (YAML structure), including providers (LLMs), prompts, and initial deterministic assertions (pass/fail checks). Practice utilizing variables within prompts. Integration: Testing commercial and local models via PromptFoo. Homework: Practice building test suites and reviewing assertions documentation.
Week 2 (Foundation & Tool Mastery)
Day 3: Prompt Engineering and Cost Evaluation
Prompt Engineering: Defining the rules and constraints of the system (System Prompt) and crafting effective test inputs (User Prompt). Using AI (LLMs) to generate effective test prompts. Evaluation: Hands-on workshop on LLM cost evaluation (budgeting) by running prompts against multiple models to compare cost per request. Organization: Structuring the testing framework using file-based prompt configurations.
Day 4: Advanced Assertion & Career Prep
Advanced Testing: Deep dive into assertions, particularly Model-Graded Assertions (MGA), where an LLM acts as a judge (LLM Rubric) to evaluate output quality (relevancy, factuality). Testing using CSV-based files for structured test data. Career Start: Introduction to LinkedIn Personal Branding; documenting early achievements and incorporating AI testing keywords (e.g., prompt engineering, LLM testing) to profiles. Homework: Review PromptFoo Red Teaming documentation.
Week 3 (Application Testing & Advanced Techniques
Day 5: Red Teaming Concepts
Equator Point: Course halfway review. Red Teaming: Defining red teaming as simulating adversarial inputs (like a comprehensive baseline report) to find vulnerabilities (e.g., security, bias). Discussion of vulnerability frameworks like the OWASP Top 10 for AI. Strategy: Understanding Red Teaming workflow (defining strategy, execution, analysis) and configurations. Comparison of red teaming types (Small, Large, XXL/Extensive). Homework: Prepare application details required for WeOptimize red teaming configuration (using AI assistance).
Day 6: Testing a Real Application (WeOptimize)
Application Architecture: Reviewing the high-level architecture of the WeOptimize application (Orchestrator, Guard LLM, specialized LLMs, knowledge bases like Jira/Confluence/Figma). Testing Mode: Focusing on end-to-end black box exploratory testing via the application's chat interface. Using the provided "Source of Truth" as acceptance criteria. Bug Reporting: Hands-on exercise reporting and documenting bugs in ClickUp, including reproduction steps and linking them to specific AI vulnerabilities.
Week 4 (Application Testing & Advanced Techniques
Day 7: Red Teaming Execution & Triage
Application Setup: Finalizing the Red Teaming configuration by inputting a comprehensive application context (main purpose, features, system rules) into PromptFoo. Group Triage: Teams exchange reported bugs and attempt to reproduce and validate issues found by classmates. Advanced Testing: Hands-on session applying PromptFoo for complex scenarios, including multi-turn conversation testing using JSON objects for regression. Tool Exposure: Alternative testing tool.
Day 8: Tool Exploration & Post-Launch Monitoring
Post-Launch Tools: Demo and discussion of tools used for monitoring and maintaining LLM models pre- and post-launch (e.g., ATARO wrapper). New Tool Assignment: Introduction to Agenta (a comparable, alternative LLM testing platform). Homework: Explore and evaluate Agenta to apply foundational testing concepts learned from PromptFoo. Research other AI testing tools in the market (consulting mindset).
Week 5 (Career Acceleration)
Day 9: Agenta Review & LinkedIn Strategy
Tool Comparison: Reviewing homework findings on Agenta, applying foundational concepts (Evals, variables) learned from PromptFoo to a new platform. Career Branding: Strategies for content creation and influence building on LinkedIn. Using generative AI tools (e.g., Claude, ChatGPT) as brainstorming partners for posts, while avoiding generic copy-paste content. Accomplishments: Workshop focused on drafting AI LLM testing accomplishment statements for resumes/profiles, quantifying the business impact of skills learned and bugs found in WeOptimize. Homework: Post tailored AI testing content on LinkedIn and engage (comment/repost) with classmates' posts.
Day 10: Final Optimization & Interview Prep
Final Profile Optimization: Updating LinkedIn profiles and resumes with core AI testing skills (prompt engineering, red teaming, hallucination detection, token consumption). Interview Preparation: Review of common AI testing interview questions (e.g., scaling tests, verifying factual responses, token consumption, security, testing LLMs with other LLMs). Wrap-up: Final remarks, community engagement commitment, and discussion of post-course resources.
Benefits You Won't Find Anywhere Else
Benefits You Won't Find Anywhere Else
Benefits You Won't Find Anywhere Else
Why learning AI & LLM Testing is a must for Every QA in 2025?
Lifetime Community Access
Lifetime Community Access
Lifetime Community Access
Join our Discord community with 600+ QA professionals.
Ongoing support from instructors and alumni.
Regular follow-up sessions and career guidance.
Recorded Sessions
Recorded Sessions
Recorded Sessions
All sessions recorded and can be accessed up to for 1 year.
Review program materials and session recording anytime.
Never miss important concepts.
Real Resume Experience
Real Resume Experience
Real Resume Experience
Work on WeOptimize, a US-based AI startup company.
Reference real US-based company projects in interviews.
Get personalized CV review.
Career Transformation Support
Career Transformation Support
Career Transformation Support
Resume and LinkedIn profile optimization.
Interview preparation and skill positioning.
Network with fellow AI testing professionals.
Who Should Attend?
01
QA Engineers, SDETs, and Test Leads ready to upskill for the AI era.
02
Automation experts are curious about testing non-deterministic AI systems.
03
Tech professionals looking to future-proof their careers as AI reshapes quality engineering.
Who Should Attend?
01
QA Engineers, SDETs, and Test Leads ready to upskill for the AI era.
02
Automation experts are curious about testing non-deterministic AI systems.
03
Tech professionals looking to future-proof their careers as AI reshapes quality engineering.
Who Should Attend?
01
QA Engineers, SDETs, and Test Leads ready to upskill for the AI era.
02
Automation experts are curious about testing non-deterministic AI systems.
03
Tech professionals looking to future-proof their careers as AI reshapes quality engineering.
Who Should Attend?
01
QA Engineers, SDETs, and Test Leads ready to upskill for the AI era.
02
Automation experts are curious about testing non-deterministic AI systems.
03
Tech professionals looking to future-proof their careers as AI reshapes quality engineering.
A recruiter messaged me saying she is impressed with my resume and skills in AI testing during the second week of the course. I got a new job as a QA Manager at that startup. All thanks to this AI & LLM Testing course at Engenious University
Sofiia Tsapchuk
Quality Manager at Company Sage
My team failed to validate our AI system 4 times - we were stuck and didn't know where to start. After the AI Testing course, I now have the tools and confidence to eliminate unknown risks. I can finally set up proper AI testing frameworks that leadership trusts.
Hershal Walton,
Product Manager at TIAA
This training has been amazing fun, interactive, and full of hands-on exercises instead of boring lectures. I actually felt sad it was ending! I came here because I saw job descriptions for AI Test Engineers that I couldn’t even understand — and I realized I needed to catch up.
Shiva Srinivasan
QA Lead at Clari Health
The sessions showed me how to easily test different LLM models which is a game-changer for protecting company data in a closed environment. The hands-on approach helped me see how to apply AI testing directly in my work, and it sparked new ideas for improving our apps and APIs at GoTo Foods.
Sanchit Agarwal
QE Architect at GoTo Foods
A recruiter messaged me saying she is impressed with my resume and skills in AI testing during the second week of the course. I got a new job as a QA Manager at that startup. All thanks to this AI & LLM Testing course at Engenious University
Sofiia Tsapchuk
Quality Manager at Company Sage
My team failed to validate our AI system 4 times - we were stuck and didn't know where to start. After the AI Testing course, I now have the tools and confidence to eliminate unknown risks. I can finally set up proper AI testing frameworks that leadership trusts.
Hershal Walton,
Product Manager at TIAA
This training has been amazing fun, interactive, and full of hands-on exercises instead of boring lectures. I actually felt sad it was ending! I came here because I saw job descriptions for AI Test Engineers that I couldn’t even understand — and I realized I needed to catch up.
Shiva Srinivasan
QA Lead at Clari Health
The sessions showed me how to easily test different LLM models which is a game-changer for protecting company data in a closed environment. The hands-on approach helped me see how to apply AI testing directly in my work, and it sparked new ideas for improving our apps and APIs at GoTo Foods.
Sanchit Agarwal
QE Architect at GoTo Foods
A recruiter messaged me saying she is impressed with my resume and skills in AI testing during the second week of the course. I got a new job as a QA Manager at that startup. All thanks to this AI & LLM Testing course at Engenious University
Sofiia Tsapchuk
Quality Manager at Company Sage
My team failed to validate our AI system 4 times - we were stuck and didn't know where to start. After the AI Testing course, I now have the tools and confidence to eliminate unknown risks. I can finally set up proper AI testing frameworks that leadership trusts.
Hershal Walton,
Product Manager at TIAA
This training has been amazing fun, interactive, and full of hands-on exercises instead of boring lectures. I actually felt sad it was ending! I came here because I saw job descriptions for AI Test Engineers that I couldn’t even understand — and I realized I needed to catch up.
Shiva Srinivasan
QA Lead at Clari Health
The sessions showed me how to easily test different LLM models which is a game-changer for protecting company data in a closed environment. The hands-on approach helped me see how to apply AI testing directly in my work, and it sparked new ideas for improving our apps and APIs at GoTo Foods.
Sanchit Agarwal
QE Architect at GoTo Foods
The sessions showed me how to easily test different LLM models which is a game-changer for protecting company data in a closed environment. The hands-on approach helped me see how to apply AI testing directly in my work, and it sparked new ideas for improving our apps and APIs at GoTo Foods.
Sanchit Agarwal
QE Architect at GoTo Foods
My team failed to validate our AI system 4 times - we were stuck and didn't know where to start. After the AI Testing course, I now have the tools and confidence to eliminate unknown risks. I can finally set up proper AI testing frameworks that leadership trusts.
Hershal Walton
Product Manager at TIAA
A recruiter messaged me saying she is impressed with my resume and skills in AI testing during the second week of the course. I got a new job as a QA Manager at that startup. All thanks to this AI & LLM Testing course at Engenious University
Sofiia Tsapchuk
QA Manager at Company Sage
This training has been amazing fun, interactive, and full of hands-on exercises instead of boring lectures. I actually felt sad it was ending! I came here because I saw job descriptions for AI Test Engineers that I couldn’t even understand — and I realized I needed to catch up.
Shiva Srinivasan
QA Lead at Clari Health
The sessions showed me how to easily test different LLM models which is a game-changer for protecting company data in a closed environment. The hands-on approach helped me see how to apply AI testing directly in my work, and it sparked new ideas for improving our apps and APIs at GoTo Foods.
Sanchit Agarwal
QE Architect at GoTo Foods
My team failed to validate our AI system 4 times - we were stuck and didn't know where to start. After the AI Testing course, I now have the tools and confidence to eliminate unknown risks. I can finally set up proper AI testing frameworks that leadership trusts.
Hershal Walton
Product Manager at TIAA
A recruiter messaged me saying she is impressed with my resume and skills in AI testing during the second week of the course. I got a new job as a QA Manager at that startup. All thanks to this AI & LLM Testing course at Engenious University
Sofiia Tsapchuk
QA Manager at Company Sage
This training has been amazing fun, interactive, and full of hands-on exercises instead of boring lectures. I actually felt sad it was ending! I came here because I saw job descriptions for AI Test Engineers that I couldn’t even understand — and I realized I needed to catch up.
Shiva Srinivasan
QA Lead at Clari Health
Instructors
Instructors
100% money back guarantee
100% Money-Back Guarantee: If you're not satisfied by Week 1, claim a full refund, no questions.
Seats are limited to 20 registrants. Secure your spot today.
100% money back guarantee
100% Money-Back Guarantee: If you're not satisfied by Week 1, claim a full refund, no questions.
Seats are limited to 20 registrants. Secure your spot today.
100% money back guarantee
100% Money-Back Guarantee: If you're not satisfied by Week 1, claim a full refund, no questions.
Seats are limited to 20 registrants. Secure your spot today.
100% money back guarantee
100% Money-Back Guarantee: If you're not satisfied by Week 1, claim a full refund, no questions.
Seats are limited to 20 registrants. Secure your spot today.
FAQs
FAQs
What is the AI Career Accelerator program?
What is the duration of the training?
Who is this program for?
What will I achieve by completion?
What’s the weekly breakdown?
Are there prerequisites?
What makes this program different?
Which modules include RAG pipelines, grounding validation, and hallucination detection?
Do we cover data quality validation and production monitoring (data drift, model drift, feedback loops)?
Is there coverage beyond text-only LLMs (e.g., CV or audio)?
Do we work with agent frameworks like AutoGen, LangGraph, or CrewAI?
What portfolio deliverables will I graduate with?
What are the system requirements to join?
What career paths does this program prepare me for?
How do I apply?
Is there a Payment Plan Option?
Will Engenious University help with Career Search?
What is the AI Career Accelerator program?
What is the duration of the training?
Who is this program for?
What will I achieve by completion?
What’s the weekly breakdown?
Are there prerequisites?
What makes this program different?
Which modules include RAG pipelines, grounding validation, and hallucination detection?
Do we cover data quality validation and production monitoring (data drift, model drift, feedback loops)?
Is there coverage beyond text-only LLMs (e.g., CV or audio)?
Do we work with agent frameworks like AutoGen, LangGraph, or CrewAI?
What portfolio deliverables will I graduate with?
What are the system requirements to join?
What career paths does this program prepare me for?
How do I apply?
Is there a Payment Plan Option?
Will Engenious University help with Career Search?
What is the AI Career Accelerator program?
What is the duration of the training?
Who is this program for?
What will I achieve by completion?
What’s the weekly breakdown?
Are there prerequisites?
What makes this program different?
Which modules include RAG pipelines, grounding validation, and hallucination detection?
Do we cover data quality validation and production monitoring (data drift, model drift, feedback loops)?
Is there coverage beyond text-only LLMs (e.g., CV or audio)?
Do we work with agent frameworks like AutoGen, LangGraph, or CrewAI?
What portfolio deliverables will I graduate with?
What are the system requirements to join?
What career paths does this program prepare me for?
How do I apply?
Is there a Payment Plan Option?
Will Engenious University help with Career Search?
What is the AI Career Accelerator program?
What is the duration of the training?
Who is this program for?
What will I achieve by completion?
What’s the weekly breakdown?
Are there prerequisites?
What makes this program different?
Which modules include RAG pipelines, grounding validation, and hallucination detection?
Do we cover data quality validation and production monitoring (data drift, model drift, feedback loops)?
Is there coverage beyond text-only LLMs (e.g., CV or audio)?
Do we work with agent frameworks like AutoGen, LangGraph, or CrewAI?
What portfolio deliverables will I graduate with?
What are the system requirements to join?
What career paths does this program prepare me for?
How do I apply?
Is there a Payment Plan Option?
Will Engenious University help with Career Search?
What is the AI Career Accelerator program?
What is the duration of the training?
Who is this program for?
What will I achieve by completion?
What’s the weekly breakdown?
Are there prerequisites?
What makes this program different?
Which modules include RAG pipelines, grounding validation, and hallucination detection?
Do we cover data quality validation and production monitoring (data drift, model drift, feedback loops)?
Is there coverage beyond text-only LLMs (e.g., CV or audio)?
Do we work with agent frameworks like AutoGen, LangGraph, or CrewAI?
What portfolio deliverables will I graduate with?
What are the system requirements to join?
What career paths does this program prepare me for?
How do I apply?
Is there a Payment Plan Option?
Will Engenious University help with Career Search?