AI for Developers courses

• Understand generative AI, large language models, tokens, embeddings, context windows, and the architectural concepts that underpin modern AI-powered software systems.
• Explore how AI models are integrated into software applications through APIs and SDKs, and understand the role of model endpoints, requests, responses, and application-side orchestration.
• Evaluate the strengths and limitations of AI-generated code, including correctness, maintainability, security, hallucination risks, and the need for developer verification.
• Build the technical foundation required to design, develop, and evaluate AI-powered applications within modern software engineering environments.

• Use AI coding assistants to generate, complete, explain, refactor, document, and improve code across common software development tasks.
• Transform requirements, user stories, and natural-language specifications into implementation plans, technical components, and initial code structures.
• Accelerate repetitive development activities while applying appropriate review practices to preserve correctness, readability, maintainability, and engineering quality.
• Develop effective AI-assisted workflows across requirements analysis, implementation, documentation, testing, review, maintenance, and other stages of the software development lifecycle.

• Design reliable prompts, system instructions, and application-level directives that clearly define the expected behavior of AI-powered software components.
• Apply structured prompting, examples, constraints, role definitions, and output schemas to improve consistency and control model responses.
• Manage context effectively by combining application data, conversation history, retrieved information, user state, and external knowledge without unnecessarily consuming the context window.
• Create reusable prompt and context-engineering patterns suitable for production applications, including versioning, testing, fallback behavior, and structured response handling.

See also: Prompt Engineering courses →

• Use AI to identify potential defects, interpret failures, explain unexpected behavior, and propose targeted corrections based on code and execution context.
• Generate unit, integration, regression, boundary, and edge-case tests that strengthen software coverage and help expose failure conditions.
• Analyze logs, stack traces, error messages, runtime behavior, and related technical evidence to accelerate troubleshooting and root-cause investigation.
• Integrate AI-assisted debugging, testing, code review, and quality analysis into engineering workflows while preserving developer validation and reliable quality controls.

• Integrate large language models into web, mobile, backend, and service-oriented applications using appropriate APIs, SDKs, and application architectures.
• Implement structured outputs, streaming responses, conversation state, model configuration, and other interaction patterns required by modern LLM applications.
• Design robust application logic around probabilistic AI responses by introducing validation, retries, fallback mechanisms, error handling, and deterministic software controls.
• Manage latency, token consumption, model selection, reliability, rate limits, and API costs when operating AI capabilities in real applications.

• Understand embeddings, vector representations, semantic search, retrieval, chunking strategies, indexing, and the architecture of Retrieval-Augmented Generation systems.
• Connect large language models with documents, databases, enterprise repositories, and organizational knowledge sources to provide relevant external context.
• Build applications that retrieve appropriate information before generation so that responses are grounded in available evidence rather than relying exclusively on model knowledge.
• Evaluate retrieval quality, answer accuracy, source attribution, citation behavior, context relevance, and hallucination risks across RAG pipelines.

See also: RAG Systems courses →

• Understand agentic AI concepts including tool calling, planning, memory, state management, iterative execution, and multi-step task completion.
• Build agents capable of interacting with APIs, databases, software applications, code execution environments, and other external tools through controlled interfaces.
• Design single-agent and multi-agent workflows that coordinate specialized capabilities to complete complex software and business tasks.
• Implement permissions, validation, execution limits, observability, approval checkpoints, and human oversight mechanisms that improve the reliability and safety of agent behavior.

See also: AI Agents courses →

• Apply AI across CI/CD pipelines, technical documentation, code review, issue management, monitoring, incident response, and other software engineering operations.
• Automate repetitive engineering workflows using AI-powered tools and agents while clearly defining the boundaries between automated actions and developer-controlled decisions.
• Analyze logs, system behavior, operational metrics, alerts, and technical evidence with AI to accelerate troubleshooting and incident investigation.
• Integrate AI into DevOps workflows while preserving reliability, traceability, reproducibility, security, approval mechanisms, and engineering control.

• Understand prompt injection, data leakage, insecure model outputs, hallucinations, excessive permissions, unsafe tool use, and other vulnerabilities specific to AI-enabled applications.
• Design systematic evaluations that measure accuracy, reliability, robustness, safety, task completion, and application-level performance under realistic conditions.
• Implement guardrails, input and output validation, monitoring, access controls, human-in-the-loop mechanisms, and other safeguards appropriate to application risk.
• Apply responsible development practices that support secure, observable, testable, and trustworthy AI applications suitable for production environments.

See also: AI Security courses →

• Design complete AI system architectures that combine foundation models, Retrieval-Augmented Generation, agents, APIs, databases, application services, and user-facing components.
• Evaluate trade-offs between prompting, retrieval, fine-tuning, agentic approaches, deterministic software, and traditional machine-learning techniques according to the problem being solved.
• Optimize AI systems for scalability, latency, cost, observability, security, reliability, maintainability, and operational performance.
• Build and deploy an end-to-end AI application using production engineering practices that address testing, monitoring, versioning, failure handling, governance, and continuous improvement.