Data Science

Training Course on Prompt Engineering and Large Language Models Optimization

Introduction

Training Course on Prompt Engineering & LLM Optimization: Advanced Techniques for Maximizing LLM Performance is designed to equip professionals with the cutting-edge skills required to master prompt engineering and Large Language Model (LLM) optimization. In today's rapidly evolving AI landscape, the ability to craft effective prompts and fine-tune LLM performance is paramount for unlocking transformative business value, driving innovation, and gaining a significant competitive advantage. Participants will delve into advanced techniques beyond basic prompt creation, focusing on strategic LLM interaction, bias mitigation, cost efficiency, and scalable deployment.

This comprehensive program moves beyond theoretical concepts, emphasizing hands-on application and real-world scenarios. Through practical exercises, case studies, and expert-led sessions, attendees will learn to harness the full potential of Generative AI, understand the intricacies of LLM architecture, implement advanced prompting strategies like Chain-of-Thought and RAG, and optimize models for speed, accuracy, and reduced inference costs. This course is crucial for anyone looking to elevate their proficiency in the AI development lifecycle and become a leader in the era of Intelligent Automation.

Programme Curriculum

Training Course on Prompt Engineering & LLM Optimization: Advanced Techniques for Maximizing LLM Performance

Introduction

Course Duration

10 days

Course Objectives

Master Advanced Prompt Crafting for optimal LLM response generation.
Implement Contextual Prompting and Multi-turn Conversations for enhanced dialogue flow.
Apply Zero-shot, Few-shot, and Chain-of-Thought (CoT) Prompting to complex problem-solving.
Understand and mitigate LLM Hallucinations and Bias Detection for reliable AI outputs.
Optimize LLMs for Inference Speed and Cost Reduction through techniques like quantization and pruning.
Utilize Retrieval Augmented Generation (RAG) for factual accuracy and up-to-date information.
Develop Prompt Chaining and Agentic Workflows for complex task automation.
Implement Ethical AI Principles in prompt design and LLM deployment.
Evaluate LLM Performance Metrics and establish robust testing methodologies.
Explore Fine-tuning and Knowledge Distillation for custom LLM applications.
Design Meta-Prompts for self-improving and adaptive LLM interactions.
Leverage LLM Observability and Prompt Management Systems for collaborative development.
Drive Business Innovation and Process Automation using optimized LLM solutions.

Organizational Benefits

Streamline workflows and automate complex tasks with highly optimized LLM applications.
Gain deeper insights from data through accurate and reliable LLM outputs.
Optimize LLM inference, leading to significant savings on computational resources.
Deliver more relevant, accurate, and personalized AI interactions.
Reduce the incidence of hallucinations and biases, ensuring more trustworthy AI deployments.
Empower teams to rapidly prototype and deploy sophisticated AI-powered solutions.
Stay ahead in the AI arms race by mastering advanced LLM optimization techniques.
Build LLM applications that can scale efficiently to meet growing demands.

Target Audience

AI/ML Engineers & Data Scientists
Software Developers
Product Managers.
Business Analysts.
Researchers & Academics.
Technical Architects
UX/UI Designers (with technical interest).
AI Enthusiasts & Innovators

Course Outline

Module 1: Foundations of Advanced Prompt Engineering

Deep Dive into LLM Mechanics & Architectures (e.g., Transformers, Attention Mechanisms)
Understanding Prompt Dynamics: Beyond Basic Inputs
The Role of Context, Persona, and Style in Prompt Design
Introduction to Prompt Evaluation Metrics
Case Study: Optimizing Customer Service Chatbots for Empathetic Responses.

Module 2: Advanced Prompt Crafting Techniques

Zero-shot, Few-shot, and One-shot Learning in Practice
Mastering Chain-of-Thought (CoT) and Tree-of-Thought (ToT) Prompting
Implementing Self-Consistency and Self-Correction Strategies
Instruction Tuning and Reinforcement Learning from Human Feedback (RLHF) for Prompt Refinement
Case Study: Improving Code Generation Accuracy for specific programming languages.

Module 3: Retrieval Augmented Generation (RAG) Implementation

Architecting RAG Systems: Indexing, Retrieval, and Generation
Choosing and Optimizing Vector Databases for RAG
Strategies for Chunking and Embedding External Knowledge
Handling Query Ambiguity and Enhancing Retrieval Relevance
Case Study: Building a Domain-Specific Q&A System for Legal Documents.

Module 4: LLM Optimization for Performance & Efficiency

Understanding LLM Inference: Speed vs. Quality Trade-offs
Quantization Techniques (e.g., 8-bit, 4-bit) for Model Compression
Pruning and Sparsity for Reduced Model Size
Batching and Parallel Processing for High Throughput
Case Study: Reducing Inference Latency in a Real-time Translation Application.

Module 5: Fine-tuning and Customization

Parameter-Efficient Fine-Tuning (PEFT) techniques like LoRA and QLoRA
Data Preparation and Curation for Fine-tuning Datasets
Strategies for Adapting Pre-trained LLMs to Specific Tasks
Evaluating Fine-tuned Model Performance
Case Study: Customizing an LLM for Niche Industry Jargon in Healthcare.

Module 6: Mitigating Bias and Hallucinations

Identifying and Analyzing Sources of Bias in LLMs
Techniques for Debiasing Prompt Responses
Strategies for Detecting and Correcting Hallucinations
Ethical Considerations in Prompt Engineering and LLM Deployment
Case Study: Ensuring Fairness in AI-driven Recruitment Tools.

Module 7: Prompt Chaining and Agentic Workflows

Designing Multi-step Prompt Sequences for Complex Tasks
Building Autonomous Agents with LLMs for Workflow Automation
Integrating LLMs with External Tools and APIs
Error Handling and Fallback Mechanisms in Agentic Systems
Case Study: Automating Research and Content Synthesis for Marketing Campaigns.

Module 8: Advanced Context Management

Managing Long Context Windows and Context Overflow
Techniques for Summarizing and Condensing Information for Prompts
Dynamic Context Injection and Retrieval
Memory and State Management in Conversational AI
Case Study: Enhancing Long-form Dialogue in a Virtual Assistant.

Module 9: Prompt Engineering for Specific Use Cases

Creative Content Generation (Storytelling, Poetry, Marketing Copy)
Code Generation and Debugging with LLMs
Data Analysis and Insights Extraction
Personalized Recommendations and Customer Engagement
Case Study: Generating Hyper-Personalized Product Descriptions for E-commerce.

Module 10: LLM Observability and Monitoring

Key Metrics for LLM Performance Monitoring (Latency, Throughput, Quality)
Logging and Tracing LLM Interactions
Identifying and Debugging Prompt Failures
A/B Testing Prompt Variations and Model Versions
Case Study: Setting up an Observability Dashboard for an LLM-powered Application.

Module 11: Prompt Management and Version Control

Establishing Best Practices for Prompt Organization
Versioning Prompts and Tracking Changes
Collaborative Prompt Development Workflows
Integrating Prompt Management with MLOps Pipelines
Case Study: Managing a Large Library of Prompts for an Enterprise-wide AI Solution.

Module 12: Meta-Prompting and Self-Improving LLMs

Understanding the Concept of Meta-Prompts
Using LLMs to Generate and Refine Prompts
Automated Prompt Optimization Techniques
Iterative Improvement of Prompt Performance
Case Study: Developing a Self-Optimizing Content Generation System.

Module 13: Security and Privacy in LLM Applications

Protecting Sensitive Data in Prompts and Outputs
Mitigating Prompt Injection Attacks
Ensuring Data Governance and Compliance with LLMs
Anonymization and De-identification Techniques
Case Study: Securing an LLM for Financial Data Analysis.

Module 14: Scaling LLM Deployment & Infrastructure

Deployment Strategies for Optimized LLMs (On-premise, Cloud, Edge)
Load Balancing and Auto-scaling for LLM Services
Containerization and Orchestration (Docker, Kubernetes) for LLMs
Cost Management and Resource Allocation for LLM Inference
Case Study: Deploying a High-Traffic LLM-powered Search Engine.

Module 15: Future Trends & Advanced Research

The Latest in LLM Architectures and Breakthroughs
Emerging Prompt Engineering Paradigms (e.g., Cognitive Architectures)
Multimodal LLMs and Their Applications
The Future of AI Agents and Autonomous Systems
Case Study: Exploring the Potential of LLMs in Scientific Discovery.

Training Methodology

This course employs a blended learning approach combining:

Interactive Lectures: Concise presentations of core concepts and advanced theories.
Hands-on Workshops: Practical coding exercises and prompt crafting sessions using leading LLM platforms (e.g., OpenAI, Anthropic, Hugging Face models).
Live Demonstrations: Expert-led walkthroughs of complex prompt engineering and optimization techniques.
Case Study Analysis: In-depth discussion and problem-solving based on real-world industry applications.
Group Discussions & Peer Learning: Collaborative problem-solving and knowledge sharing.
Individual & Team Projects: Application of learned skills to develop practical LLM solutions.
Q&A Sessions: Dedicated time for addressing participant queries and fostering deeper understanding.

Register as a group from 3 participants for a Discount

Send us an email: info@fineskilltrainingcenter.org or call +254769199797

Certification

Upon successful completion of this training, participants will be issued with a globally- recognized certificate.

Tailor-Made Course

We also offer tailor-made courses based on your needs.

Key Notes

a. The participant must be conversant with English.

b. Upon completion of training the participant will be issued with an Authorized Training Certificate

c. Course duration is flexible and the contents can be modified to fit any number of days.

d. The course fee includes facilitation training materials, 2 coffee breaks, buffet lunch and A Certificate upon successful completion of Training.

e. One-year post-training support Consultation and Coaching provided after the course.

f. Payment should be done at least a week before commence of the training, to Fineskill Training Center account, as indicated in the invoice so as to enable us prepare better for you.

Programme Fee

Duration: 10 Days

Method: Online or Physical

Online Price: $2,000

Physical Price: $3,000

Certification: Accredited Certificate

View Available Dates 📄 Download Course Outline (PDF)

Available Sessions

Location

Month

Year

Oct 19 2026

19 Oct — 30 Oct 2026

💻 Online / Virtual session

Online $2,000