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Duration 7 hours
Course Outline
LLM Application Architecture and Design
- Exploring common OpenAI application patterns for assistants, copilots, and automated workflows.
- Selecting optimal architectural structures based on business requirements, reliability needs, and user experience goals.
- Transitioning from initial prototypes to sustainable, maintainable application designs.
Prompting, Context, and Structured Outputs
- Organizing system, user, and developer instructions to ensure predictable system behavior.
- Crafting prompts that promote consistency, precise task control, and clear responses.
- Leveraging structured outputs to facilitate downstream application logic.
- Managing context windows, conversation state, and overall response quality.
Tool Use and Workflow Orchestration
- Integrating function calling and tool-enabled workflows with external services.
- Validating inputs and outputs, managing errors, and defining fallback strategies.
- Designing multi-step processes to address complex business tasks.
Retrieval and Knowledge Grounding
- Determining appropriate use cases for retrieval-augmented generation.
- Preparing documents and segmenting content for effective retrieval processes.
- Retrieving pertinent context to ground responses in trusted sources.
Evaluation, Guardrails, and Operational Readiness
- Establishing quality benchmarks and testing workflows against defined outcomes.
- Mitigating hallucinations and managing unsafe, irrelevant, or ambiguous requests.
- Monitoring usage patterns, latency, token consumption, and associated costs.
- Preparing applications for deployment, ongoing support, and continuous improvement.
Hands-On Implementation Workshop
- Developing a complete end-to-end OpenAI application that integrates prompting, structured output, tool usage, and retrieval capabilities.
- Analyzing design choices, addressing common pitfalls, and identifying practical steps for production deployment.
Requirements
- Working knowledge of large language model concepts and API-centric application development.
- Practical experience with REST APIs, JSON data structures, and prompt-driven application logic.
- Intermediate proficiency in Python, JavaScript, or an equivalent programming language.
Target Audience
- Software developers engaged in building LLM-centric applications.
- AI engineers and technical leaders designing solutions based on OpenAI platforms.
- Product teams and solution architects accountable for delivering production-grade AI features.
