Thank you for sending your enquiry! One of our team members will contact you shortly.
Thank you for sending your booking! One of our team members will contact you shortly.
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, reliability standards, and user experience goals.
- Transitioning from prototype code to maintainable application design.
Prompting, Context, and Structured Outputs
- Structuring system, user, and developer instructions to ensure predictable behavior.
- Designing prompts for consistency, task control, and clearer responses.
- Utilizing structured outputs to support downstream application logic.
- Managing context windows, conversation state, and response quality.
Tool Use and Workflow Orchestration
- Leveraging function calling and tool-enabled workflows with external services.
- Validating inputs and outputs, handling errors, and implementing fallback mechanisms.
- 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 for 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.
- Reducing hallucinations and managing unsafe, irrelevant, or ambiguous requests.
- Monitoring usage, latency, token consumption, and cost.
- Preparing applications for deployment, support, and iterative improvement.
Hands-On Implementation Workshop
- Building a small end-to-end OpenAI application that combines prompting, structured output, tool use, and retrieval.
- Reviewing design decisions, common issues, and practical next steps for production use.
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 similar language.
Audience
- Software developers creating LLM-powered applications.
- AI engineers and technical leads designing OpenAI-based solutions.
- Product teams and solution architects responsible for production AI features.
7 Hours
