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Course Outline
LLM Application Architecture and Design
- Explore common OpenAI application patterns, including assistants, copilots, and workflow automation.
- Select appropriate architectures based on business requirements, reliability, and user experience.
- Transition from prototype code to maintainable application design.
Prompting, Context Management, 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 Integration and Workflow Orchestration
- Utilize function calling and tool-enabled workflows with external services.
- Validate inputs and outputs, manage errors, and implement fallback behaviors.
- Design multi-step flows to address practical business tasks.
Retrieval-Augmented Generation and Knowledge Grounding
- Identify scenarios where 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.
- Mitigate hallucinations and handle unsafe, irrelevant, or ambiguous requests.
- Monitor usage, latency, token consumption, and costs.
- Prepare applications for deployment, support, and iterative improvement.
Hands-On Implementation Workshop
- Construct a small end-to-end OpenAI application combining prompting, structured output, tool use, and retrieval.
- Review design decisions, address common issues, and outline practical next steps for production use.
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 similar language.
Target Audience
- Software developers creating LLM-powered applications.
- AI engineers and technical leads designing solutions based on OpenAI.
- Product teams and solution architects managing production AI features.
7 Hours
