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Course Outline
LLM Application Architecture and Design
- Explore common OpenAI application patterns for assistants, copilots, and workflow automation.
- Select the appropriate architecture based on business needs, reliability requirements, and user experience.
- Transition from prototype code to scalable, maintainable application designs.
Prompting, Context, and Structured Outputs
- Structure system, user, and developer instructions to ensure predictable behavior.
- Design prompts for consistency, task control, and clearer responses.
- Leverage structured outputs to support downstream application logic.
- Manage context windows, conversation state, and overall response quality.
Tool Use and Workflow Orchestration
- Integrate function calling and tool-enabled workflows with external services.
- Validate inputs and outputs, handle errors, and implement fallback behaviors.
- Design multi-step flows for practical business tasks.
Retrieval and Knowledge Grounding
- Determine when retrieval-augmented generation is suitable.
- Prepare documents and chunk content for effective retrieval.
- Retrieve relevant context and ground responses in trusted sources.
Evaluation, Guardrails, and Operational Readiness
- Define quality criteria and test workflows against expected outcomes.
- Minimize hallucinations and manage unsafe, irrelevant, or ambiguous requests.
- Monitor usage, latency, token consumption, and associated costs.
- Prepare applications for deployment, support, and continuous improvement.
Hands-On Implementation Workshop
- Develop a complete end-to-end OpenAI application integrating prompting, structured outputs, tool use, and retrieval.
- Review design decisions, address common issues, and outline practical steps for production deployment.
Requirements
- Understanding of large language model concepts and API-based application development.
- Experience with REST APIs, JSON, and prompt-driven application workflows.
- Intermediate programming proficiency in Python, JavaScript, or a comparable language.
Audience
- Software developers creating LLM-powered applications.
- AI engineers and technical leads designing OpenAI-based solutions.
- Product teams and solution architects responsible for implementing production AI features.
7 Hours
