Building LLM Apps with Prompt Engineering

Prerequisites

Participants should have:

• Familiarity with basic programming fundamentals such as functions, variables, and control flow
• Experience writing simple scripts in a language such as Python
• A general understanding of APIs and working with external libraries

Target audience

This course is designed for:

• Developers and engineers looking to integrate LLMs into products or internal applications
• Technical professionals exploring AI inference and generative AI use cases
• Organisations seeking to build applied capability in AI, Cloud, and Data technologies

Building LLM Apps with Prompt Engineering Course Content

Introduction to large language models and AI inference

• Overview of large language models and transformer-based architectures
• Understanding tokens, context windows, and inference
• Common enterprise use cases for LLMs
• The role of AI inference in production systems
• Positioning LLMs within AI, Cloud, and Data strategies

Foundations of prompt engineering

• What prompt engineering is and why it matters
• Instruction design and task specification
• Zero-shot, one-shot, and few-shot prompting
• Structuring prompts for clarity, consistency, and control
• Managing tone, format, and output constraints

Iterative prompt optimisation

• Evaluating LLM outputs against task requirements
• Techniques for refining and debugging prompts
• Chain-of-thought and structured reasoning prompts
• Using system and user messages effectively
• Establishing repeatable prompt patterns for applications

Working with NVIDIA NIM and Llama 3.1

• Overview of NVIDIA NIM architecture and capabilities
• Accessing and configuring an NVIDIA language model NIM endpoint
• Interacting with the Llama 3.1 model for inference tasks
• Performance considerations and scaling inference workloads
• Integrating NIM into application backends

Building LLM workflows with LangChain

• Introduction to the LangChain framework
• Creating prompt templates and reusable components
• Managing memory and conversational state
• Composing chains for multi-step reasoning tasks
• Orchestrating tools and external data sources

Generative applications and document analysis

• Designing text generation workflows for content and automation
• Summarisation and information extraction from long documents
• Chunking strategies and context management
• Building pipelines for large-scale document processing
• Validating and post-processing LLM outputs

Chatbot assistants and conversational systems

• Designing conversational flows and system prompts
• Managing dialogue context and user intent
• Handling edge cases and ambiguous queries
• Integrating chat interfaces with backend services
• Monitoring and improving chatbot performance

Foundations for advanced LLM techniques

• Introduction to retrieval-augmented generation
• When to use retrieval versus fine-tuning
• Overview of parameter-efficient fine-tuning concepts
• Security considerations, including prompt injection risks
• Governance, compliance, and responsible AI practices

Exams and assessments

Participants will complete practical coding exercises, guided labs, and knowledge checks throughout the workshop. A final applied assessment will require learners to design and implement an LLM-based application using NVIDIA NIM and LangChain.

Upon successful completion of the assessment, participants will receive an NVIDIA certificate recognising subject matter competency and supporting professional career growth.

Hands-on learning

This workshop is built around applied, hands-on learning:

• Guided labs using NVIDIA NIM and Llama 3.1
• Practical exercises building real LLM-powered features
• Collaborative problem-solving scenarios based on enterprise use cases
• Instructor feedback on prompt design and application architecture