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Course Outline
LLM Application Architecture and Design
- Standard OpenAI application patterns for assistants, copilots, and automated workflows
- Selecting optimal architectures based on business needs, reliability, and user experience
- Transitioning from prototype code to sustainable application designs
Prompting, Context, and Structured Outputs
- Organizing system, user, and developer instructions to ensure consistent behavior
- Crafting prompts for consistency, task management, and clear responses
- Leveraging structured outputs to facilitate downstream application logic
- Managing context windows, conversational state, and response integrity
Tool Usage and Workflow Orchestration
- Implementing function calling and tool-enabled workflows with external services
- Validating data inputs and outputs, managing errors, and defining fallback strategies
- Designing multi-step processes for practical business tasks
Retrieval and Knowledge Grounding
- Determining when retrieval-augmented generation is the most suitable approach
- Preparing documents and segmenting content for effective retrieval
- Fetching relevant context and anchoring responses in verified sources
Evaluation, Guardrails, and Operational Readiness
- Establishing quality metrics and testing workflows against expected results
- Mitigating hallucinations and managing unsafe, irrelevant, or ambiguous requests
- Tracking usage, latency, token consumption, and associated costs
- Preparing applications for deployment, ongoing support, and continuous improvement
Practical Implementation Workshop
- Constructing a comprehensive end-to-end OpenAI application that integrates prompting, structured outputs, tool usage, and retrieval
- Analyzing design choices, addressing common challenges, and identifying actionable next steps for production deployment
Requirements
- Understanding of large language model principles and API-centric application development
- Practical experience with REST APIs, JSON data formats, and prompt-driven workflow design
- Intermediate-level proficiency in Python, JavaScript, or comparable programming languages
Target Audience
- Software developers creating LLM-integrated applications
- AI engineers and technical leaders architecting OpenAI-based solutions
- Product teams and solution architects overseeing production-ready AI features
7 Hours
