AI-Powered Modern Development

AI-Powered Modern Development

From Prompts to Secure Microservices in 2 days

Reignite creativity by mastering AI integration, modern architectures, and secure coding practices in real projects

Software engineering doesn't have to be a hamster wheel of bug fixes and cookie-cutter features. While everyone else is grinding through the same old patterns, you'll be weaponizing AI to think differently, build faster, and ship solutions that actually matter.

This isn't another theory-heavy course about "the future of development." It's about right now—using LLMs and cutting-edge tools as your secret weapon to prototype at light speed, architect with precision, and create software that stands out in a sea of sameness.

Walk away with battle-tested patterns, ready-to-deploy templates, and the kind of AI-powered workflow that turns you into the developer everyone wants on their team. Because while others are still debugging yesterday's problems, you'll be building tomorrow's solutions.

Ready to level up?

Learning Outcomes

By the end of the 2-day course, participants will be able to:

• Understand advanced prompt engineering techniques and apply them to real development tasks.
• Use LLMs to help in planning, designing, prototyping and maintaining code in Python, Go, and Java.
• Integrate AI (via APIs, local models, embeddings, etc.) into application workflows.
• Apply ML features/techniques in application development (feature stores, model serving, evaluation, feedback loops).
• Architect multi-language / microservice systems using message queues (Kafka or similar) that support AI workflows.
• Leverage modern microservices architectures to scale AI functionality (model serving, workflow orchestration, etc.).
• Embed security best practices appropriate for AI-augmented systems and microservices.
• Foster more creative problem solving using AI tools, pattern thinking, and new architectural paradigms.

Prerequisites

To get the most out of this course, participants should:

• Be comfortable programming in at least one of: Python, Go, Java.
• Have experience building and deploying non-trivial applications (i.e. some architecture exposure, microservices exposure helpful but not strictly required).
• Basic familiarity with ML/AI concepts (what a model, dataset, training vs inference are).
• Some knowledge of networked systems / APIs, message queues (not deep but basic).
• Willingness to experiment and work in teams / breakout sessions.

Training Outline

Below is the very detailed, nested outline covering both theory and hands-on / workshop-type content, for 2 days.

1. Prompt Engineering for Developers
   1. What is a prompt, what are its components (system / user / role / examples)
   2. Key prompt engineering techniques (2024-25)
      1. Zero-shot, One-shot, Few-shot prompting
      2. Chain-of-Thought, Tree-of-Thought reasoning
      3. Self-consistency & prompt voting / ensemble of outputs
      4. Meta-prompting (prompts that generate or refine other prompts)
      5. Context window design: priming, retrieval-augmented generation (RAG), prompt chaining
      6. Adversarial prompting, prompt injection risks and mitigations
   3. Tools, platforms, frameworks for prompt engineering
      1. LangChain, LlamaIndex, etc.
      2. Local vs cloud LLMs, hybrid, embeddings, fine-tuning vs prompt tuning vs soft-prompting
   4. Workshop: Given a real problem (in Python / Go / Java), participants design prompts (few-shot, chain-of-thought) to do things like code generation, refactoring, documentation, test generation.
2. Designing & Developing with LLMs / AI Helpers
   1. Use cases: code generation, code review, debugging assistance, pair programming with AI, auto completion, refactoring, documentation, translating code between languages
   2. Integrating LLMs via APIs vs hosting local models (ONNX, small LMs)
   3. Architectures for embedding AI in your dev loop (CI/CD, pre-commit, linting, auto-PRs)
   4. Feedback loops: evaluating output quality, human-in-loop corrections, continuous learning
3. Machine Learning in Application Development
   1. ML model lifecycle: Data collection, preprocessing, training, validation, deployment, monitoring
   2. Feature stores, model serving, versioning, drift detection
   3. Light ML vs heavy ML: embedding-based features, classification/regression, anomaly detection, recommendation, etc.
   4. Tools: BentoML, TFX, Kubeflow, or other relevant tools; possibly in Java (e.g. DL4J) / Go (Gorgonia, etc.)
4. Multi-Language Models & Message Queue Systems
   1. What are multi-language models in this context? (e.g. language interop, calling code in one language from another, or using models that support multiple languages)
   2. Message queues: Kafka, RabbitMQ, etc. Use cases for ML / AI pipelines (event sourcing, asynchronous processing, streaming inference)
   3. Patterns: producer/consumer, event buses, stream processing, microservice interactions over queues
   4. Ensuring consistency, dealing with failures, scalability, idempotence
5. Microservices and Modern AI Architecture
   1. Review of microservice architecture style: API gateways, service meshes, domain boundaries, bounded contexts
   2. Patterns for AI microservices: model serving services, feature store services, data pipelines, inference vs training separation
   3. Scaling, deployment (containerization, Kubernetes, serverless, batch vs real-time)
   4. Workflow orchestration (Argo, Airflow, etc.), pipeline orchestration, scheduling
   5. Observability: logging, metrics, tracing especially in AI usage (latency, mispredictions, drift)
6. Security & Safe AI / Secure Coding in the Age of AI
   1. Secure by design principles (least privilege, defense in depth, zero trust)
   2. Specific security risks with AI/LLMs: prompt injection; data leakage; privacy; model poisoning; malicious input; misuse
   3. Microservices security best practices: authentication & authorization per service; secure communication (TLS, mTLS, service mesh); API gateways; rate limiting; secret management; network segmentation
   4. Secure ML deployment: validation of inputs, adversarial robustness, securing model artifacts, versioning, monitoring for drift or malicious behavior
   5. Data privacy, regulation issues, handling PII, GDPR etc.
7. Creativity & Innovation with AI
   1. Using AI for ideation: brainstorming, prototyping, creativity sprints
   2. Hackathon / prototyping session: cross-language teams build a small project using AI + microservice + message queue + secure deployment
   3. Patterns from industry: case studies of companies doing novel things with AI + microservices (2024-2025)
   4. Encouraging experimentation: patterns for small side projects, internal tools, etc.