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Duration 7 hours
Course Outline
LLM Application Architecture and Design
- Exploring common OpenAI application patterns for assistants, copilots, and workflow automation.
- Selecting the optimal architecture to meet business requirements, reliability standards, and user experience goals.
- Transitioning from prototype code to scalable, maintainable application design.
Prompting, Context, and Structured Outputs
- Structuring system, user, and developer instructions to ensure predictable behavior.
- Crafting prompts for consistency, precise task control, and clearer responses.
- Leveraging structured outputs to facilitate downstream application logic.
- Managing context windows, conversation state, and overall response quality.
Tool Use and Workflow Orchestration
- Utilizing function calling and tool-enabled workflows to interact with external services.
- Validating inputs and outputs, managing errors, and implementing fallback behaviors.
- Designing multi-step flows to address practical business tasks.
Retrieval and Knowledge Grounding
- Determining when retrieval-augmented generation (RAG) is the appropriate approach.
- Preparing documents and chunking content to optimize retrieval effectiveness.
- Retrieving relevant context to ground responses in trusted sources.
Evaluation, Guardrails, and Operational Readiness
- Defining quality benchmarks and testing workflows against expected outcomes.
- Mitigating hallucinations and managing unsafe, irrelevant, or ambiguous requests.
- Monitoring usage metrics, latency, token consumption, and costs.
- Preparing applications for deployment, ongoing support, and iterative enhancement.
Hands-On Implementation Workshop
- Developing a small-scale, end-to-end OpenAI application that integrates prompting, structured output, tool usage, and retrieval.
- Reviewing design choices, common pitfalls, and actionable next steps for production deployment.
Requirements
- Proficiency in large language model concepts and API-based application development.
- Experience interacting with REST APIs, JSON structures, and prompt-driven application workflows.
- Intermediate programming skills in Python, JavaScript, or a comparable language.
Target Audience
- Software developers creating LLM-enabled applications.
- AI engineers and technical leads architecting OpenAI-based solutions.
- Product teams and solution architects accountable for production-grade AI features.
