AI-Powered Modern Development

1. Prompt and context design

• What is a prompt, what are its components (system / user / role / examples)
• Selected model-appropriate prompting patterns and their limitations
  • Zero-shot, One-shot, Few-shot prompting
  • Task decomposition and structured rationale where useful; compare reasoning approaches without demanding hidden chain-of-thought or assuming one universal recipe.
  • Compare multiple outputs when useful; agreement is not proof and adds cost.
  • Meta-prompting (prompts that generate or refine other prompts)
  • Context window design: priming, retrieval-augmented generation (RAG), prompt chaining
  • Adversarial prompting, prompt injection risks and mitigations
• Tools, platforms, frameworks for prompt engineering
  • Selected current LangChain/LlamaIndex interfaces for the prepared example.
  • Local/cloud/hybrid models and embeddings; distinguish prompting from model adaptation and its additional data/resource requirements.
• Workshop in a familiar language: draft/refactor/document/test a small illustrative task, then review and run checks.

2. AI-assisted development workflows

• Use cases: code generation, code review, debugging assistance, pair programming with AI, auto completion, refactoring, documentation, translating code between languages
• Integrating LLMs via APIs vs hosting local models (ONNX, small LMs)
• Architectures for embedding AI in your dev loop (CI/CD, pre-commit, linting, auto-PRs)
• Evaluate output and review corrections; feedback does not automatically train or update a model.

3. ML lifecycle and application components

• ML model lifecycle: Data collection, preprocessing, training, validation, deployment, monitoring
• Feature stores, model serving, versioning, drift detection
• Light ML vs heavy ML: embedding-based features, classification/regression, anomaly detection, recommendation, etc.
• Compare selected compatible model-serving and pipeline tools for the chosen stack, not implementation of every framework in two days.

4. Polyglot services and queues

• Polyglot services: language interoperability and shared interfaces; distinguish implementation languages from multilingual model capabilities.
• Message queues: Kafka, RabbitMQ, etc. Use cases for ML / AI pipelines (event sourcing, asynchronous processing, streaming inference)
• Patterns: producer/consumer, event buses, stream processing, microservice interactions over queues
• Ensuring consistency, dealing with failures, scalability, idempotence

5. AI microservice architecture

• Review of microservice architecture style: API gateways, service meshes, domain boundaries, bounded contexts
• Patterns for AI microservices: model serving services, feature store services, data pipelines, inference vs training separation
• Compare container, Kubernetes, serverless, batch and streaming deployment choices at an architecture level.
• Workflow/pipeline orchestration options and their operational requirements; selected demonstration.
• Observability: logging, metrics, tracing especially in AI usage (latency, mispredictions, drift)

6. Security and responsible deployment

• Secure by design principles (least privilege, defense in depth, zero trust)
• Specific security risks with AI/LLMs: prompt injection; data leakage; privacy; model poisoning; malicious input; misuse
• Microservices security best practices: authentication & authorization per service; secure communication (TLS, mTLS, service mesh); API gateways; rate limiting; secret management; network segmentation
• Secure ML deployment: validation of inputs, adversarial robustness, securing model artifacts, versioning, monitoring for drift or malicious behavior
• Data-protection and applicable regulatory questions, including GDPR where relevant, for appropriate context-specific review.

7. Prototype and review

• Using AI for ideation: brainstorming, prototyping, creativity sprints
• Team prototype: a small prepared AI/service/queue workflow with selected security checks; not a production-secure deployment claim.
• Illustrative architecture cases and trade-offs, not unverified company success claims or stale 2024–2025 rankings.
• Encouraging experimentation: patterns for small side projects, internal tools, etc.