Why Attend
As organisations increasingly integrate ChatGPT and generative artificial intelligence into business operations, customer services, knowledge management, and digital workflows, ensuring consistent performance has become a critical management responsibility. Successful AI adoption requires more than deploying a functional application; organisations must continuously evaluate response quality, operational efficiency, reliability, security, and business value.
AI-powered systems can experience performance challenges involving inaccurate responses, inconsistent outputs, excessive latency, high operational costs, inefficient prompts, and changing user requirements. Applications that perform well during initial testing may become less effective as business information, usage patterns, model versions, and operational conditions evolve.
Effective performance monitoring enables organisations to understand how AI applications behave in real-world environments, identify weaknesses, measure user satisfaction, and implement improvements based on reliable evidence. Performance optimisation helps improve response quality, reduce unnecessary processing, manage costs, and strengthen the overall user experience.
The ChatGPT: Performance Monitoring and Optimization course provides participants with a practical framework for evaluating, monitoring, and improving ChatGPT-based applications and workflows.
Participants explore AI performance indicators, prompt evaluation, response accuracy, latency measurement, token usage, cost optimisation, application observability, quality assurance, model selection, and continuous improvement.
The programme also examines AI governance, monitoring dashboards, testing methodologies, human feedback mechanisms, and the management of performance risks associated with generative AI systems.
Through practical workshops, analytical exercises, and implementation case studies, participants develop the skills needed to establish effective monitoring processes and optimise ChatGPT solutions for reliable, efficient, and responsible organisational use.
Professional Problems This Course Is Designed to Solve
Inconsistent AI response quality: Establishing systematic evaluation methods to assess accuracy, relevance, completeness, consistency, and instruction adherence.
Slow response times and poor user experience: Identifying latency bottlenecks and applying practical optimisation techniques to improve application responsiveness.
Uncontrolled AI operating costs: Monitoring token consumption, model usage, request volumes, and processing costs to improve resource efficiency.
Limited visibility into AI application performance: Developing monitoring dashboards and operational indicators that provide meaningful information about system behaviour.
Difficulty identifying AI errors and performance deterioration: Implementing testing, feedback collection, exception monitoring, and regression evaluation procedures.
Inefficient prompts and workflow configurations: Improving prompt structure, context management, model selection, and application architecture.
Weak AI governance and accountability: Establishing performance standards, monitoring responsibilities, audit trails, and continuous improvement processes.
Course Methodology
The programme combines instructor-led discussions, live demonstrations, AI performance analysis exercises, prompt optimisation workshops, application monitoring simulations, and practical case studies.
Participants work with illustrative ChatGPT application scenarios involving customer support, document processing, internal knowledge assistance, business analysis, and workflow automation.
Hands-on activities include developing performance indicators, evaluating AI-generated responses, comparing prompt variations, analysing latency and token usage, designing monitoring dashboards, and preparing optimisation recommendations.
The programme addresses both ChatGPT-based business workflows and API-integrated applications. Technical exercises may use illustrative application logs, evaluation datasets, and monitoring tools, while non-technical participants focus on performance interpretation, quality management, and operational decision-making.
Course Objectives
By the end of this course, participants will be able to:
Explain the principles of performance monitoring for ChatGPT and generative AI applications
Identify relevant AI quality, reliability, efficiency, and business performance indicators
Develop structured methods for evaluating AI-generated responses
Measure and interpret latency, throughput, token usage, and operational costs
Apply prompt optimisation techniques to improve response quality and efficiency
Evaluate model selection and configuration decisions against business requirements
Design monitoring dashboards for AI application performance
Identify errors, regressions, and changes in AI system behaviour
Establish testing and evaluation procedures for AI workflows
Apply security, privacy, and governance controls to AI performance monitoring
Develop continuous improvement processes based on operational evidence and user feedback
Prepare an integrated ChatGPT performance monitoring and optimisation framework
Target Audience
This course is suitable for:
Artificial intelligence and machine learning professionals
Digital transformation managers and specialists
IT operations and application support managers
Software developers and AI application engineers
Data analysts and business intelligence professionals
AI product managers and digital service owners
Business process improvement professionals
Customer experience and service automation managers
Technology governance and AI risk professionals
Project managers responsible for generative AI implementation
Target Competencies
Participants will develop competencies in:
Generative AI performance measurement
AI response quality evaluation
Prompt engineering and optimisation
AI latency and throughput analysis
Token usage and cost management
AI application monitoring and observability
Model evaluation and regression testing
Performance dashboard development
AI operational risk and governance
Continuous AI performance improvement
