The 100-day curriculum

• Set up your AI engineering environment the way real teams do.
• Refresh Python, APIs and testing fundamentals.
• See how input, reasoning, tools and responses connect end to end.

Stack: Python · VS Code · Git & GitHub · Postman

• Learn how LLMs reason and, more importantly, where they fail.
• Master zero-shot, few-shot, chain-of-thought and prompt chaining.
• Design structured outputs and system prompts that behave predictably.
• Engineer context windows, tokens and embeddings deliberately.

Stack: OpenAI · Claude · Gemini · DeepSeek · Pydantic

• Understand why retrieval fails and how to measurably improve accuracy.
• Design agents that answer only from approved documents.
• Build RAG and GraphRAG pipelines with real vector and graph stores.
• Ship a retrieval system you could genuinely deploy inside your company.

Stack: Pinecone · Qdrant · FAISS · Neo4j · Embeddings

• Learn when agents make sense — and when plain code is the better answer.
• Break complex work into planner, executor and reviewer roles.
• Build orchestrated workflows with LangChain and LangGraph.
• Understand coordination cost, latency budgets and failure modes.

Stack: LangChain · LangGraph · OpenAI Agents SDK

• Learn why structured tool communication beats free text at scale.
• Build and wrap MCP servers for real tools.
• Connect agents to browsers, databases and test runners safely.

Stack: MCP Protocol · Playwright MCP · FastAPI

• Generate grounded test cases from requirements and Jira stories.
• Control hallucination through grounding and structured outputs.
• Decide test count and type distribution with LLM planners.

Stack: Jira · Pydantic · Pytest · RAG

• Generate Playwright TypeScript specs from natural language.
• Solve locator uniqueness and section-scoped disambiguation.
• Use accessibility snapshots and ref-based selection over brittle XPath.
• Replace hardcoded waits with reliable dynamic wait strategies.

Stack: Playwright · Playwright MCP · TypeScript

• Design three-layer healing: generation-time, runtime and post-run patching.
• Score locator candidates with fuzzy matching against golden flows.
• Track heal history and flakiness statistics your team can act on.

Stack: DOM capture · Fuzzy scoring · Heal dashboards

• Evaluate agents at both component and system level.
• Protect agents from prompt injection and misuse.
• Add guardrails, schema validation and approval flows for risky actions.
• Build an agent safe enough for customer-facing use.

Stack: LangSmith · Ragas · DeepEval · Guardrails

• Learn when fine-tuning is worth the cost versus prompting and RAG.
• Prepare datasets for domain adaptation.
• Make trade-offs you can explain to stakeholders and architects.

Stack: Hugging Face · Datasets · Cost modelling

• Build AI services with FastAPI and package them with Docker.
• Wire CI/CD with GitHub Actions; manage secrets and configuration.
• Track latency, cost and failure rates; design rollback strategies.

Stack: FastAPI · Docker · GitHub Actions · CI/CD

• Design a complete AI quality system from scratch.
• Integrate retrieval, agents, execution, evaluation and safety.
• Deploy and test it like a real production service.
• Present and defend your architecture the way a senior engineer would.

Stack: Full stack · Live architecture review

• Practise AI system design interviews with case-based reasoning.
• Explain agent architectures, trade-offs and failure modes clearly.
• Optimise your resume and LinkedIn for AI-native roles.
• Sit mock interviews with structured behavioural coaching.

Stack: Mock panels · Resume clinic · LinkedIn audit

• Revisit classes and assignments as often as you need.
• Get guidance during your active job search.
• Convert preparation into offers.

Stack: Lifetime recordings · Community access