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 based on business requirements, reliability needs, and user experience.
- Transitioning from prototype code to maintainable application design.
Prompting, Context, and Structured Outputs
- Structuring system, user, and developer instructions for predictable behavior.
- Designing prompts to ensure 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 behaviors.
- Designing multi-step flows for practical business tasks.
Retrieval and Knowledge Grounding
- Identifying appropriate scenarios 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.
- Mitigating hallucinations and managing 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 small end-to-end OpenAI application that integrates prompting, structured output, tool use, and retrieval.
- Reviewing design decisions, common issues, and practical next 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 managing production AI features.
7 Hours
