Global Learning for Operational Leaders
Classroom
0 sessionsOnline / Live
0 sessionsIntroduction
Course overview
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.
What you will achieve
Learning objectives
- 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
Who should attend
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
Methodology
Learning approach
- 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.
Complete course outline
Five focused days of learning and application
The timings provided are estimates and may vary depending on the number of participants and the discussions taking place during the sessions. They are indicative only and do not constitute a binding commitment.
Day 1
ChatGPT Performance Fundamentals and Monitoring Frameworks
- Understanding performance management in generative AI applications and the relationship between response quality, operational efficiency, reliability, and business outcomes
- Identifying common ChatGPT performance challenges, including hallucinations, inconsistent responses, excessive latency, context limitations, and inefficient processing
- Establishing performance measurement frameworks covering quality, accuracy, relevance, response time, availability, cost, and user satisfaction
- Distinguishing performance considerations for direct ChatGPT usage, custom AI workflows, and API-integrated applications
- Coffee break
- Distinguishing performance considerations for direct ChatGPT usage, custom AI workflows, and API-integrated applications (continued)
- Defining performance baselines, service objectives, evaluation criteria, reporting responsibilities, and monitoring requirements
- Practical workshop: Developing a ChatGPT performance measurement framework with KPIs, evaluation criteria, target thresholds, and management reporting requirements
Day 2
AI Response Quality Evaluation and Prompt Optimisation
- Evaluating AI-generated responses for factual accuracy, completeness, relevance, clarity, consistency, and adherence to instructions
- Developing representative evaluation datasets and test scenarios that reflect real organisational tasks and user expectations
- Applying prompt optimisation techniques involving clear instructions, contextual information, examples, structured outputs, and task decomposition
- Coffee break
- Applying prompt optimisation techniques involving clear instructions, contextual information, examples, structured outputs, and task decomposition (continued)
- Comparing prompt variations through controlled testing and assessing improvements in response quality and token efficiency
- Understanding automated evaluation methods, human review, scoring rubrics, and the limitations of AI-assisted response assessment
- Hands-on exercise: Evaluating multiple prompt configurations and developing an improved prompt template supported by measurable quality indicators
Day 3
Latency, Token Usage, Cost Management and Technical Optimisation
- Understanding response latency, request processing time, throughput, and factors affecting AI application responsiveness
- Analysing token consumption, context size, output length, model selection, and their influence on application costs and performance
- Applying optimisation techniques such as prompt simplification, context reduction, appropriate model selection, caching where supported, and efficient workflow design
- Coffee break
- Applying optimisation techniques such as prompt simplification, context reduction, appropriate model selection, caching where supported, and efficient workflow design (continued)
- Evaluating API usage patterns, rate limits, concurrency considerations, retry mechanisms, and error handling in AI-enabled applications
- Comparing performance trade-offs involving speed, accuracy, reliability, model capability, and operating expenditure
- Practical workshop: Analysing illustrative AI application usage data and preparing a cost and latency optimisation plan with measurable performance targets
Day 4
AI Application Monitoring, Observability and Performance Dashboards
- Understanding AI application observability through operational logs, request metrics, response evaluation results, error records, and user feedback
- Designing monitoring dashboards covering request volumes, response latency, token usage, estimated costs, quality scores, and system reliability
- Identifying performance anomalies, recurring failures, unusual usage patterns, and changes in application behaviour
- Coffee break
- Identifying performance anomalies, recurring failures, unusual usage patterns, and changes in application behaviour (continued)
- Establishing alert thresholds, escalation procedures, incident investigation processes, and performance reporting cycles
- Applying regression testing to detect performance changes following prompt modifications, model updates, knowledge source changes, or workflow adjustments
- Case study: Developing an AI performance dashboard and investigating a simulated deterioration in response quality, latency, and operational efficiency
Day 5
AI Governance, Continuous Improvement and Performance Optimisation Strategy
- Establishing AI performance governance covering accountability, evaluation standards, documentation, monitoring responsibilities, and management oversight
- Evaluating privacy, security, data retention, access controls, and responsible AI considerations associated with application monitoring and operational logs
- Integrating user feedback, human evaluation, incident findings, and performance data into structured continuous improvement processes
- Coffee break
- Developing AI testing and optimisation cycles covering baseline assessment, improvement hypotheses, controlled changes, validation, and post-deployment monitoring
- Preparing an AI performance improvement roadmap that prioritises quality, reliability, cost efficiency, user experience, and measurable business benefits
- Capstone exercise: Developing and presenting a Comprehensive ChatGPT Performance Monitoring and Optimisation Framework incorporating performance KPIs, evaluation datasets, prompt improvement techniques, latency and cost analysis, monitoring dashboards, governance controls, and a continuous improvement roadmap
FAQ
Frequently asked questions
What does ChatGPT: Performance Monitoring and Optimization (DIT-023) cover?
This course covers Digital Innovation and Transformation through a structured five-day outline focused on practical application, discussion, and implementation planning.
When is the next available session?
The course schedule and fee are available on request. Contact Aractech for current availability and a quotation.
Who should attend this course?
This course is suitable for:, Artificial intelligence and machine learning professionals, Digital transformation managers and specialists
Is bilingual course delivery available?
Yes. All course materials and presentations are provided in English, while bilingual explanation and discussion support may be available depending on the selected location and trainer availability. For example, when attending a course in Istanbul, you may request a Turkish-speaking trainer to explain the English course content in Turkish. Arabic explanation support may also be requested at this location. Other languages are available in selected cities. You can choose your preferred language support as an optional preference during registration.
How can I register for a session?
Use any Register button next to the available course dates to open the participant registration page and submit your booking request for the selected session.
Is this course available online as well as classroom-based?
The schedule and fee, including any available delivery options, are available on request. Contact Aractech for details.
Where are classroom sessions delivered?
Classroom venues are confirmed in the schedule panel for each published session.
How long is the training programme?
The programme runs for five days, with up to five training hours per day, from 9:00 AM to 2:00 PM. Each programme includes a total of 25 training hours.
Quick request
Send quick request
Send a fast enquiry about this course and the academy team will get back to you.
Get a quotation
Request a formal quotation
Choose an available date and venue, add your participants, and we will email a formal quotation for ChatGPT: Performance Monitoring and Optimization to your business address.