Building LLM Applications using Prompt Engineering
- Description
- Curriculum
- Reviews
Building LLM Applications using Prompt Engineering is a practical, beginner-friendly course designed to help learners understand how modern Large Language Models (LLMs) can be used to create intelligent applications through effective prompt engineering techniques and AI development workflows.
The course introduces the core concepts behind Large Language Models and explores how these models are transforming industries through conversational AI, automation, content generation, knowledge retrieval, and intelligent assistants. Learners will understand how to design prompts, integrate APIs, and build AI-powered systems capable of solving real-world problems.
Throughout the course, students will move from foundational concepts to practical implementation. They will explore prompt engineering strategies, learn how to interact with language models programmatically, and understand how conversational workflows are developed using APIs.
The course also introduces advanced concepts including Retrieval-Augmented Generation (RAG), fine-tuning techniques, and training workflows that help learners understand how modern AI systems are customized and improved for specialized use cases.
By combining conceptual learning with implementation-focused lessons, this course provides learners with the skills required to build modern LLM-powered applications.
Course Objectives
This course aims to help learners:
- Understand Large Language Models and their applications
- Learn prompt engineering principles and best practices
- Build AI applications using language model APIs
- Develop conversational workflows using LLMs
- Explore advanced prompting techniques
- Understand Retrieval-Augmented Generation (RAG) concepts
- Learn model customization approaches
- Understand fine-tuning and training workflows
What You Will Learn
Fundamentals of LLM Applications
- Understanding Large Language Models
- AI-powered application architectures
- Real-world LLM use cases
- Components of intelligent systems
Prompt Engineering Concepts
- Prompt structure and design
- Zero-shot, one-shot, and few-shot prompting
- Prompt optimization strategies
- Improving model reliability and consistency
Building AI Applications
- Setting up development environments
- Working with APIs
- Creating conversational systems
- Managing context and interactions
Advanced LLM Techniques
- Retrieval-Augmented Generation (RAG)
- Fine-tuning concepts
- Training workflows
- External knowledge integration
Skills You Will Gain
By completing this course, learners will develop skills in:
- Prompt engineering
- LLM application development
- API integration
- Conversational AI design
- Context management
- AI workflow design
- Retrieval-based systems
- Model customization concepts
Who This Course Is For?
This course is suitable for:
- Beginners interested in Generative AI
- Developers exploring LLM applications
- Students learning AI technologies
- Software engineers building AI tools
- Data science and machine learning learners
- Anyone interested in prompt engineering
Prerequisites
Before taking this course, learners should have:
- Basic computer knowledge
- Interest in Artificial Intelligence
- Basic programming knowledge (helpful but not mandatory)
- No prior LLM experience required
Course Outcome
By the end of this course, learners will understand how modern language models work, how prompt engineering affects model performance, and how intelligent applications can be built using APIs, prompting techniques, retrieval systems, and conversational workflows. Students will gain practical knowledge that can be applied to real-world AI projects and future advanced LLM development.
-
1
1. Introduction to Building Different LLM applicationsThis lesson introduces Large Language Model (LLM) applications and explores how modern AI systems are transforming industries through intelligent text generation, automation, and conversational interfaces. Learners will understand the foundations of LLM-powered systems and their practical applications.
-
2
2. Prompt Engineering
This lesson introduces prompt engineering techniques used to control, optimize, and improve outputs generated by large language models through structured instruction design.
-
3
3. Retrieval Augmented Generation
Learn how Retrieval-Augmented Generation combines language models with external knowledge sources to improve factual accuracy, provide updated information, and enhance application reliability.
-
4
4. Finetuning LLMs
This lesson introduces fine-tuning methods used to customize pre-trained language models for specialized tasks and domain-specific applications.
-
5
5. Training LLMs from Scratch
This lesson provides an overview of building large language models from the ground up, covering training pipelines, computational requirements, and development challenges.
-
6
1. Introduction to Prompt Engineering
This lesson introduces prompt engineering and explains how carefully designed prompts influence the quality, accuracy, and usefulness of outputs generated by large language models.
-
7
2. Set up your machine for Prompt Engineering
This lesson guides learners through preparing their development environment for prompt engineering by setting up tools, libraries, APIs, and configurations required for LLM experimentation.
-
8
3. Prompt Engineering with ChatGPT API
This lesson introduces learners to interacting with language models programmatically using APIs and demonstrates how prompts can be integrated into applications.
-
9
4. Enabling Conversation with ChatGPT API
This lesson focuses on building conversational experiences using APIs by managing context, conversation history, and multi-turn interactions.
-
10
1. Introduction to Understanding Different Prompt Engineering Techniques
This lesson introduces various prompt engineering techniques and explains how different prompting approaches influence the quality, reliability, and behavior of large language models.
-
11
2. Few Shot Prompting
This lesson introduces Few-Shot Prompting, a technique where multiple examples are provided to guide language models toward producing more accurate and structured outputs.
-
12
3. One Shot Prompting
This lesson focuses on One-Shot Prompting, where a single example is provided to help guide model behavior and improve output quality.
-
13
4. Zero Shot Prompting
This lesson introduces Zero-Shot Prompting, a technique where tasks are performed using instructions alone without providing examples.
North America
Digilancer, LLC
99 Wall Street #4786
New York, NY 10005
Europe
Digilancer LTD. (UK)
85 Great Porland Street
London, England W1W 7LT
Australia
Digilancer Pty Ltd
12/2 Classic Way
Burleigh Waters, QLD 4220
Middle East
Digilancer (SMC-PRIVATE) Ltd
22/23, Corniche Road
Phase 4, Bahria Town, 44000.
Digilancer offers a dynamic ecosystem tailored to foster creativity, collaboration, and growth. We’re a premier destination with the perfect environment for individuals and businesses to innovative and thrive.
© Digilancer. All Rights Reserved.
DMCA Notice: Digilancer is committed to safeguarding intellectual property rights and ensuring a respectful online environment. We expect all visitors and users of our services to uphold the same values. In accordance with the Digital Millennium Copyright Act of 1998 (DMCA), Digilancer takes copyright infringement seriously and will take decisive action against any party that infringes upon Digilancer’s services and/or the Digilancer website (referred to as the “Site”).
Trademark Disclaimer: Digilancer is a registered trademark property of Digilancer. All third-party trademarks (including logos and icons) referenced by Digilancer remain the property of their respective owners. Unless specifically identified as such, Digilancer’s use of third-party trademarks does not indicate any relationship, sponsorship, or endorsement between Digilancer and the owners of these trademarks. Any references by Digilancer to third-party trademarks are to identify the corresponding third-party goods and/or services and shall be considered nominative fair use under trademark law.