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Course Outline
LLM Application Architecture and Design
- Exploring common OpenAI patterns for building assistants, copilots, and automated workflows.
- Selecting appropriate architectures to meet business needs, reliability standards, and user experience goals.
- Transitioning from experimental prototypes to sustainable, maintainable application designs.
Prompt Engineering, Context Management, and Structured Outputs
- Crafting system, user, and developer instructions to ensure consistent behavior.
- Developing prompts for precise task control and clarity in responses.
- Leveraging structured outputs to facilitate downstream application logic.
- Managing context windows, conversation state, and response fidelity.
Tool Integration and Workflow Orchestration
- Implementing function calling and tool-driven workflows to interact with external services.
- Validating data inputs and outputs, managing errors, and defining fallback mechanisms.
- Designing multi-step processes to handle complex business tasks.
Retrieval and Knowledge Grounding
- Determining when retrieval-augmented generation (RAG) is the optimal approach.
- Preparing documentation and segmenting content for effective retrieval.
- Fetching relevant context to ground responses in verified, trustworthy sources.
Evaluation, Safety Guardrails, and Operational Excellence
- Establishing quality benchmarks and testing workflows against expected outcomes.
- Mitigating hallucinations and addressing unsafe, irrelevant, or ambiguous user requests.
- Monitoring usage metrics, latency, token consumption, and overall costs.
- Preparing applications for deployment, ongoing support, and continuous improvement.
Hands-On Implementation Workshop
- Building a complete end-to-end OpenAI application that integrates prompting, structured outputs, tool usage, and retrieval.
- Analyzing architectural decisions, troubleshooting common issues, and planning next steps for production deployment.
Requirements
- Working knowledge of large language model (LLM) concepts and API-centric application development.
- Proficiency in interacting with REST APIs, JSON data structures, and prompt-driven application logic.
- Intermediate-level programming skills in Python, JavaScript, or an equivalent language.
Intended Audience
- Software engineers developing LLM-integrated products.
- AI specialists and technical leads architecting OpenAI-based solutions.
- Product teams and solution architects accountable for shipping production-ready AI features.
7 Hours
