Prompt Engineering for QA
Turn testing intent into clear, reusable instructions with observable outputs.
Ship: Publish a prompt pack for requirements, test design, defect triage and reporting.
56 DAYS / Beginner to advanced / SAVED PROGRESS
Generative AI, automation and agents for software testers
Move from prompt and LLM fundamentals to AI-generated QA artifacts, agent and MCP workflows, advanced AI evaluation, and a reviewable testing portfolio.
- 56
- Days
- 04
- Phases
- 82
- Topics
- 7-9 hours
- Per week
MISSION ROUTE
4 phases. One working portfolio.
Learn - build - prove - advance
OPTIONAL LIVE COHORTS
Add guided acceleration
Follow the complete roadmap free, or pair each specialization with live instruction, projects and mentor feedback.
Field progress
Phase 1 / Days 1-14
AI Fundamentals and Prompt Engineering
Learn how prompts and LLMs behave before using them to influence test design, code or release decisions.
Exit evidence
A versioned QA prompt library with zero-shot, few-shot and structured prompting examples plus a hallucination test sheet.
0/18
Phase topics complete
LLMs and Testing Tools
Compare model behavior and choose tools based on evidence instead of novelty.
Ship: Run one QA task across three models and document quality, latency and privacy tradeoffs.
Phase 2 / Days 15-28
Generation with AI for QA
Apply AI to requirements, plans, cases, APIs, reports and performance work while checking every generated artifact.
Exit evidence
A traceable QA evidence pack generated from one product brief and reviewed against acceptance criteria.
0/23
Phase topics complete
AI-Assisted Test Design
Use AI to accelerate analysis without outsourcing risk decisions.
Ship: Create a reviewed test strategy, checklist, case table and defect report from one requirement set.
API Tests and Automation
Generate API assets that remain maintainable, secure and executable.
Ship: Build and review one Postman or REST Assured API suite with typed data and negative checks.
Reporting and Performance
Convert generated checks into explainable delivery evidence.
Ship: Publish an Allure report and an AI-assisted load-test plan with explicit assumptions.
Phase 3 / Days 29-42
AI Agents, MCP and Test Automation
Build tool-using workflows that can explain, change and test code within explicit permissions and evidence gates.
Exit evidence
An n8n or code-based agent connected to a constrained MCP toolset, with traces and acceptance tests.
0/23
Phase topics complete
n8n and AI Agents
Orchestrate useful QA tasks while keeping each tool action inspectable.
Ship: Automate requirement intake, test generation and review as a traced agent workflow.
MCP Servers for Testers
Let models use tools through contracts that are narrow, testable and revocable.
Ship: Connect an LLM to one local MCP server and test its capability and permission boundaries.
AI-Assisted Automation Framework
Improve a real Selenium codebase without accepting opaque generated architecture.
Ship: Document and improve one Maven, TestNG and Allure framework through reviewable AI changes.
Phase 4 / Days 43-56
Advanced AI Tools and Integration
Combine functional automation, synthetic data, analytics and LLM evaluation, then present the work responsibly.
Exit evidence
A six-project portfolio with DeepEval evidence, an AI-assisted automation demo and a reviewed career pack.
0/18
Phase topics complete
Advanced AI Testing Systems
Test both conventional software and AI behavior through measurable workflows.
Ship: Ship one end-to-end AI feature evaluation with a dataset, metrics, failure examples and CI gate.
Career Workflows with AI
Use AI to improve presentation without fabricating experience or evidence.
Ship: Publish a truthful, role-specific resume, LinkedIn profile and project narrative backed by repository evidence.
8-WEEK EXECUTION PLAN
Ship proof every week
Use weekdays to learn and practice. Use the final session of each week to produce the artifact listed under ship.
- 01Days 1-7
Prompting foundations
Prompt types, few-shot examples, reusable frameworks and QA instructions.
Ship
A versioned prompt library for four common QA workflows.
- 02Days 8-14
Models, tools and factuality
Model comparison, local models, coding tools, privacy and hallucination testing.
Ship
A three-model comparison with factuality failures and review rules.
- 03Days 15-21
AI-assisted QA analysis
Requirements, test strategy, plans, cases, defects and closure reports.
Ship
A reviewed test evidence pack generated from one product brief.
- 04Days 22-28
API generation and reporting
Postman, REST Assured, Python, data builders, auth, Allure and load plans.
Ship
An executable API suite with report evidence and negative checks.
- 05Days 29-35
n8n and agent workflows
Agent orchestration, code review, repair, optimization and traced tool use.
Ship
A traced requirement-to-test workflow with a human acceptance gate.
- 06Days 36-42
MCP and automation frameworks
MCP contracts, scopes, side effects and AI-assisted Selenium architecture.
Ship
A constrained MCP demo and one reviewed framework improvement.
- 07Days 43-49
Advanced AI quality
Functional automation, synthetic data, ReportPortal and DeepEval.
Ship
An LLM evaluation suite with datasets, metrics and failure examples.
- 08Days 50-56
Portfolio and career proof
Project documentation, resume validation, LinkedIn and interview stories.
Ship
A six-project portfolio and a career pack grounded in repository evidence.
DEFINITION OF DONE
What day 56 should prove
Completion is based on working evidence, not consumed lessons. Your repository, traces and CI runs should make these claims verifiable.
- Write reusable prompts and evaluation criteria for real QA work
- Generate and review test plans, test cases, bug reports and API checks with AI
- Build n8n and MCP workflows with explicit tool and permission boundaries
- Use AI to explain, repair and improve Java and test automation code
- Evaluate LLM behavior with DeepEval, factuality and safety checks
- Ship six documented AI-testing projects plus an evidence-led career portfolio
QABATTLE FIELD NOTES
Read alongside the roadmap
LLM hallucination detection
Test factuality and capture useful hallucination failures.
Testing AI agents
Evaluate tools, memory, trajectories and task completion.
Testing MCP servers
Verify tool schemas, permissions, contracts and failure handling.
DeepEval tutorial
Build repeatable LLM evaluation cases and metrics.
Prompt injection testing
Add adversarial checks to AI and agent workflows.
Trace and evaluate AI agents
Turn execution traces into measurable agent-quality evidence.
FIELD BRIEFING
Roadmap FAQ
Can a software tester follow this AI roadmap without coding experience?
Yes. The first two weeks establish prompt and LLM fundamentals. Coding becomes more important in the API, agent and automation phases, where you can begin with guided Java, REST Assured or Python examples.
Why is this roadmap 56 days?
The supplied roadmap has four substantial phases but no fixed schedule. This implementation gives each phase two weeks, producing an eight-week plan with one concrete artifact every week.
Does it include AI agents and MCP servers?
Yes. Days 29 through 42 cover n8n agent workflows, code review and repair, MCP tools and resources, permissions, argument validation, side effects and test automation integration.
Does the roadmap cover LLM evaluation?
Yes. The final phase covers DeepEval, golden datasets, factuality, relevance, hallucination, prompt injection and evidence-led CI acceptance.
What should the AI testing portfolio contain?
Aim for six small but complete projects: prompt regression, AI-assisted test design, API generation, an n8n agent, an MCP workflow and a DeepEval-backed AI feature evaluation.
Start with the first proof
Complete day 1, commit the exercise, and let evidence compound for 56 days.