Break Into AI Testing

Yes — at least 1 year of QA experience (manual or automation).

No programming background is needed, though familiarity with testing workflows is helpful.

Submit your application and confirm your eligibility — only 50 seats per cohort are available. Early applicants receive priority for personalized feedback and project pairing.

Not sure if the program is right for you? Book a free AI Career Strategy call before you enroll.

Yes, currently available for U.S. and Canada applicants. May be available for other countries - only for U.S. Dollars payments.

During checkout, you can select a payment plan through Stripe’s Klarna interface, allowing you to spread tuition into manageable installments.

Attend Week 1. If it is not right for you, claim a full refund under the published Week 1 money-back guarantee. The price is the price, however you pay.

AI Career Accelerator is a long term path aimed at full career transformation that offers various courses about AI testing and internship opportunities.

"Break Into AI Testing" - is a first step of AI Career Accelerator path, a 5-week, hands-on training program designed to help QA engineers transition into AI & LLM Testing roles. You’ll work on a real U.S. startup AI project while mastering model evaluation, red-teaming, and test automation with AI tools.

QA professionals with at least 1 year of experience, manual or automation, who want to move into AI testing. That includes QA engineers, SDETs and automation engineers, manual testers, QA leads and managers, and software engineers moving toward AI quality roles.

No coding or AI experience is required. Curiosity, analytical thinking and a testing mindset matter more.

These are addressed through:

• Deterministic & weighted assertions

• LLM-graded accuracy evaluation

• Safety, bias, and hallucination detection patterns

• Multi-model comparison

• Context-based grounding checks

Weeks 2–3 focus on advanced Promptfoo assertions and red-team strategies to identify hallucinations, factual drift, and grounding violations.

You won’t build a RAG pipeline from scratch, but you’ll learn how to evaluate retrieval-augmented systems — a core QA responsibility in AI production environments.

You’ll gain practical skills to:

1. Test and validate AI-powered applications and LLMs

2. Detect hallucinations, bias, and factual drift

3. Evaluate grounding and context reliability

4. Use frameworks like Promptfoo and LLM-graded assertions

5. Build a portfolio-ready capstone project aligned with current job roles

You’ll explore multi-agent orchestration concepts by testing a live AI app.

We emphasize end-to-end testing rather than isolated stages.

You’ll learn to:

✅ Identify failure points in multi-turn interactions

✅ Evaluate guardrail effectiveness and memory behavior

✅ Detect safety leaks and context loss across chained logic

✅ This reflects real QA work in AI product teams — black-box testing of complex reasoning flows.

Yes — these are included through:

1. Drift indicators and re-evaluation cycles
2. Synthetic variation testing
3. Failure pattern analysis
4. Feedback loop triage

You’ll learn to identify regression behaviors and emergent defects as AI systems evolve — essential for real-world QA teams.

Week 1: LLM fundamentals, your lab, and your first Promptfoo suite.

Week 2: prompt engineering, cost evaluation, and LLM-as-judge assertions.

Week 3: red-teaming strategy and OWASP Top 10 for AI against a real application.

Week 4: full red-team execution, group bug triage, multi-turn regression, and post-launch monitoring.

Week 5: tool comparison, accomplishment statements, résumé and LinkedIn, and interview prep.

Not in this course. The "Break Into AI Testing" bootcamp focuses exclusively on text-based LLMs, since the current job market is centered on grounding, factuality, and safety validation for text systems.

For alumni, the next step on the AI Career Accelerator path is AI Security Engineer: Red Teaming & Agentic AI Defense — a 5-week specialization covering LLM applications, RAG pipelines, MCP tools, memory and autonomous agents.

1. Project-based learning: You test a real U.S. AI startup product

2. 95% hands-on: Minimal theory, maximum practice — three to four hands-on activities every session

3. Around eight people supporting every cohort: instructors, a host and volunteers rotating between the breakout groups, so you are never stuck alone

4. You leave with artifacts, not notes: a GitHub repository of everything you built, which is what you demo in an interview. Sessions are also recorded and available for a year if you miss one

5. Career coaching and interview prep built into the final week

Nothing is mandatory. The tools we teach on — Promptfoo, LM Studio, Agenta — are free, as are GitHub and Discord.

Two things we strongly recommend, because they make the work considerably smoother:

✅ An Anthropic (Claude) subscription, around $20/month. This is the one we'd push hardest for.

✅ OpenRouter, roughly $10–20, if you want access to less rate-limited models. There is a free tier, but it is throughput-limited, so performance degrades when you are working at pace.

The OpenAI and Anthropic APIs are pay-as-you-go rather than subscriptions — you will need your own key and a small credit balance. Budget around $10 of API credit to start; most students spend little more than that during the cohort.

Every session is recorded and stays available for a year. The program is project-based, so keep pace with the assignments and use the recordings to catch up promptly — most students also revisit them during the internship and while preparing for interviews.

Yes. Join the Engenious Discord and ask graduates directly about the workload, the group labs, the instructors, the internship and the transition. Do not take our word for it.

Yes. You test a real U.S. startup AI product and use that work to build your evaluation report and your portfolio story. The specific product can vary by cohort.

The training is 5 weeks. It includes 10 lectures (40 hours). Classes run Saturdays and Sundays, 10 am – 2 pm Pacific. Sessions typically run four to five hours — we don't cut them short when a group is mid-exercise.

During the week you have homework you complete at your own pace. Plan on at least 10–12 hours a week in total — four hours Saturday, four Sunday, and the rest weekday lab work. That is the floor. Students who finish with a portfolio and a job are usually closer to 15–18 hours: they form a study group with two or three others, redo the exercises, and post as they go.

💻 Windows

✅ Windows 10 (64-bit) or newer

✅ Intel Core i5/i7 or AMD Ryzen 5/7

✅ 16 GB RAM or higher

✅ 30 GB free SSD space

✅ Dedicated GPU with 6 GB+ VRAM recommended for local models (e.g. NVIDIA RTX 2060/3060)

✅ Node.js v18+, Python 3.8+, VS Code, Git (Docker optional)

✅ Chrome or Edge browser

✅ Stable 10 Mbps+ internet + webcam

🍏 macOS: macOS Monterey (12+) or newer

✅ Apple Silicon (M1, M2, M3 or M4) — Intel-only Macs are not supported

✅ 16 GB RAM or higher

✅ 30 GB free SSD space

✅ Homebrew, Node.js v18+, Python 3.8+ (Docker optional)

✅ Chrome or Safari browser

✅ Reliable 10 Mbps+ connection + webcam

Only have 8 GB of RAM?

It is not a dealbreaker. Only Week 1 is affected — the exercise where we run models locally. You can point that exercise at a cloud API instead, which the course uses throughout anyway. Promptfoo, red teaming, Agenta and every live session run against APIs, so they are unaffected by how much memory you have.

That said, if you plan to make a career of building, evaluating or attacking AI systems, plan to upgrade at some point — some roles require running models on your own machine.

💡 Tip: Dual-monitor setups improve productivity for labs and evaluations.

No, and nobody honest can promise one. What we can tell you is what actually happens: on average 38% of our alumni are in a job in the roles we teach within nine months, against an industry benchmark nearer 10%.

Worth understanding why that number isn't higher, because it's the honest part — not everyone who joins is looking for a job. Many enrol to upskill and are happy where they are, and some land roles or promotions and never tell us. 38% is what we can actually evidence.

The entire final week is dedicated to career work — LinkedIn and personal branding, job-search strategy for the AI and QA markets, résumé and portfolio positioning, and interview preparation on the questions actually being asked now. You also start posting from Week 2, so the search is working for you before you finish.

Beyond the cohort, we partner with recruiting agencies specialising in AI evaluation, AI testing and cyber security, and we recommend our top students to them — a recommendation, not a placement. For top-performing graduates, Engenious sometimes offers short-term contract work through partner projects; timing and availability are never guaranteed.

After the programme, instructors stay generally available in our Discord at no cost. One-to-one mentorship sessions are available separately as a paid add-on.

Yes, as an optional next step. About three weeks after the bootcamp ends, you can join a 4-week unpaid internship testing a real AI product. It is open to every student who finishes the bootcamp and registers for it.

The three-week gap is deliberate: it gives you time to rewatch the recordings and keep practising before you start.