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Course Outline
LLM Application Architecture and Design
- Common OpenAI application patterns for assistants, copilots, and workflow automation.
- Selecting the appropriate architecture to meet business requirements, ensure reliability, and enhance user experience.
- Transitioning from prototype code to maintainable application design.
Prompting, Context, and Structured Outputs
- Structuring system, user, and developer instructions to achieve predictable behavior.
- Designing prompts that ensure consistency, task control, and clearer responses.
- Leveraging structured outputs to support downstream application logic.
- Managing context windows, conversation state, and response quality.
Tool Use and Workflow Orchestration
- Utilizing function calling and tool-enabled workflows with external services.
- Validating inputs and outputs, handling errors, and applying fallback behaviors.
- Designing multi-step flows for practical business tasks.
Retrieval and Knowledge Grounding
- Identifying appropriate use cases for retrieval-augmented generation.
- Preparing documents and chunking content to facilitate effective retrieval.
- Retrieving relevant context and grounding responses in trusted sources.
Evaluation, Guardrails, and Operational Readiness
- Defining quality criteria and testing workflows against expected outcomes.
- Mitigating hallucinations and handling unsafe, irrelevant, or ambiguous requests.
- Monitoring usage, latency, token consumption, and costs.
- Preparing applications for deployment, support, and iterative improvement.
Hands-On Implementation Workshop
- Building a complete end-to-end OpenAI application that integrates prompting, structured outputs, tool use, and retrieval.
- Reviewing design decisions, common issues, and practical next steps for production deployment.
Requirements
- Familiarity with large language model concepts and API-based application development.
- Experience working with REST APIs, JSON, and prompt-driven application workflows.
- Intermediate programming proficiency in Python, JavaScript, or a comparable language.
Audience
- Software developers creating applications powered by LLMs.
- AI engineers and technical leads designing solutions based on OpenAI.
- Product teams and solution architects responsible for implementing production-ready AI features.
7 Hours
