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Course Outline
Large Language Model Application Architecture and Design Principles
- Establishing standard application patterns for intelligent assistants, collaborative copilots, and automated workflows
- Selecting optimal architectural frameworks to meet business objectives, ensure system reliability, and enhance user experience
- Translating initial prototypes into sustainable, maintainable application architectures
Prompt Engineering, Context Management, and Structured Data Output
- Organizing system, user, and developer directives to ensure consistent and predictable model behavior
- Crafting prompts that maximize task precision, control, and response clarity
- Leveraging structured outputs to facilitate integration with downstream application logic
- Oversight of context windows, conversation states, and overall response quality metrics
Tool Integration and Workflow Orchestration
- Implementing function calling and tool-enabled processes to interface with external services
- Enforcing input and output validation, error management, and fallback protocols
- Architecting multi-stage workflows for complex operational tasks
Information Retrieval and Knowledge Grounding
- Determining the applicability of retrieval-augmented generation methodologies
- Processing documentation and segmenting content to optimize retrieval efficiency
- Accessing pertinent context and anchoring responses in verified, trusted information sources
Performance Evaluation, Safety Guardrails, and Operational Readiness
- Establishing quality benchmarks and validating workflows against defined expected outcomes
- Mitigating hallucinations and addressing unsafe, irrelevant, or ambiguous user requests
- Monitoring utilization rates, latency, token expenditure, and cost efficiency
- Preparing applications for deployment, ongoing support, and iterative refinement
Practical Implementation Laboratory
- Developing a comprehensive end-to-end application that integrates prompting, structured output, tool utilization, and retrieval mechanisms
- Analyzing architectural choices, identifying common challenges, and defining actionable steps for production environments
Requirements
- Proficiency in large language model concepts and API-based application development
- Practical experience working with REST APIs, JSON structures, and prompt-driven application workflows
- Intermediate programming proficiency in Python, JavaScript, or equivalent languages
Target Audience
- Software developers engaged in the construction of LLM-powered applications
- AI engineers and technical leaders responsible for designing OpenAI-based solutions
- Product teams and solution architects overseeing production AI features
7 Hours
