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Prompt Engineering with LLM Course
Prompt Engineering with LLM Course
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Live Online Classes starting on 28th Jun 2025
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Why enroll for Prompt Engineering with LLM Training Course?
Prompt Engineering with LLM Course Benefits
The global LLM market is anticipated to grow at a CAGR of 35.92% from 2025 to 2033, with 80% of enterprises adopting LLMs and prompt engineering for seamless automation and content creation. As businesses embrace these technologies, demand for experts in LLM optimization and prompt design is soaring. Our course empowers you with cutting-edge expertise to thrive in this fast-growing field at the forefront of AI innovation.
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Why Prompt Engineering with LLM Training Course from edureka
Live Interactive Learning
- World-Class Instructors
- Expert-Led Mentoring Sessions
- Instant doubt clearing
Lifetime Access
- Course Access Never Expires
- Free Access to Future Updates
- Unlimited Access to Course Content
24x7 Support
- One-On-One Learning Assistance
- Help Desk Support
- Resolve Doubts in Real-time
Hands-On Project Based Learning
- Industry-Relevant Projects
- Course Demo Dataset & Files
- Quizzes & Assignments
Industry Recognised Certification
- Edureka Training Certificate
- Graded Performance Certificate
- Certificate of Completion
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About your Prompt Engineering with LLM Training Course
Skills Covered
Tools Covered
Prompt Engineering with LLM Course Curriculum
Curriculum Designed by Experts
Generative AI Essentials
14 Topics
Topics
- What is Generative AI?
- Generative AI Evolution
- Differentiating Generative AI from Discriminative AI
- Types of Generative AI
- Generative AI Core Concepts
- LLM Modelling Steps
- Transformer Models: BERT, GPT, T5
- Training Process of an LLM Model like ChatGPT
- The Generative AI development lifecycle
- Overview of Proprietary and Open Source LLMs
- Overview of Popular Generative AI Tools and Platforms
- Ethical considerations in Generative AI
- Bias in Generative AI outputs
- Safety and Responsible AI practices
![skill]( "Hands On Experience")
- Creating a Small Transformer using PyTorch
- Explore OpenAI Playground to test text generation
![skill]( "skill you will learn")
- Generative AI Fundamentals
- Transformer Architecture
- LLM Training Process
- Responsible AI Practices
Prompt Engineering Essentials
10 Topics
Topics
- Introduction to Prompt Engineering
- Structure and Elements of Prompts
- Zero-shot Prompting
- One-shot Prompting
- Few-shot Prompting
- Instruction Tuning Basics
- Prompt Testing and Evaluation
- Prompt Pitfalls and Debugging
- Prompts for Different NLP Tasks (Q&A, Summarization, Classification)
- Understanding Model Behavior with Prompt Variations
![skill]( "Hands On Experience")
- Craft effective zero-shot, one-shot, and few-shot prompts
- Write prompts for different NLP tasks: Q&A, summarization, classification
- Debug poorly structured prompts through iterative testing
- Analyze prompt performance using prompt injection examples
![skill]( "skill you will learn")
- Prompt Structuring
- Prompt Tuning
- Task-Specific Prompting
- Model Behavior Analysis
Advanced Prompting Techniques
14 Topics
Topics
- Chain-of-Thought (CoT) Prompting
- Tree-of-Thought (ToT) Prompting
- Self-Consistency Prompting
- Generated Knowledge Prompting
- Step-back Prompting
- Least-to-Most Prompting
- Adversarial Prompting & Prompt Injection
- Defenses against Prompt Injection
- Auto-prompting techniques
- Semantic Search for Prompt Selection
- Context Window Optimization strategies
- Dealing with ambiguous prompts
- Human-in-the-loop prompt refinement
- Prompt testing and validation methodologies
![skill]( "Hands On Experience")
- Implementing CoT and ToT
- Testing Prompt Robustness
- Auto-Prompt Generation
- Human-in-the-loop Refinement
![skill]( "skill you will learn")
- Multi-step Prompting
- Prompt Injection Defense
- Semantic Prompt Optimization
- Prompt Evaluation Techniques
Working with LLM APIs and SDKs
10 Topics
Topics
- LLM Landscape: OpenAI, Anthropic, Gemini, Mistral API, LLaMA
- Core Capabilities: Summarization, Q&A, Translation, Code Generation
- Key Configuration Parameters: Temperature, Top_P, Max_Tokens, Stop Sequences
- Inference Techniques: Sampling, Beam Search, Greedy Decoding
- Efficient Use of Tokens and Context Window
- Calling Tools
- Functions With LLMs
- Deployment Considerations for Open-Source LLMs (Local, Cloud, Fine-Tuning)
- Rate Limits, Retries, Logging
- Understanding Cost, Latency, and Performance and Calculating via Code
![skill]( "Hands On Experience")
- API Calls with OpenAI, Gemini, Anthropic
- Tuning Parameters for Text Generation
- Token Usage Optimization
![skill]( "skill you will learn")
- API Integration
- Parameter Tuning
- Inference Techniques
- Cost Optimization
Building LLM Apps with LangChain and LlamaIndex
9 Topics
Topics
- LangChain Overview
- LlamaIndex Overview
- Building With LangChain: Chains, Agents, Tools, Memory
- Understanding LangChain Expression Language (LCEL)
- Working With LlamaIndex: Document Ingestion, Index Building, Querying
- Integrating LangChain and LlamaIndex: Common Patterns
- Using External APIs and Tools as Agents
- Enhancing Reliability: Caching, Retries, Observability
- Debugging and Troubleshooting LLM Applications
![skill]( "Hands On Experience")
- Building Chains and Agents
- Indexing with LlamaIndex
- External API Integration
- Observability Implementation
![skill]( "skill you will learn")
- LangChain Workflows
- Document Indexing
- Tool Integration
Developing RAG Systems
14 Topics
Topics
- What is RAG and Why is it Important?
- Addressing LLM limitations with RAG
- The RAG Architecture: Retriever, Augmenter, Generator
- DocumentLoaders
- Embedding Models in RAG
- VectorStores as Retrievers in LangChain and in Llamaindex
- RetrievalQA Chain and its variants
- Customizing Prompts for RAG
- Advanced RAG Techniques: Re-ranking retrieved documents
- Query Transformations
- Hybrid Search
- Parent Document Retriever and Self-Querying Retriever
- Evaluating RAG Systems: Retrieval Metrics
- Evaluation Metrics for Generation
![skill]( "Hands On Experience")
- Build a RAG Pipeline
- Implement RetrievalQA With Custom Prompts
- Evaluate Retrieval and Generation Quality Using Standard Metrics
![skill]( "skill you will learn")
- AG Architecture Understanding
- Document Retrieval Techniques
- Prompt Customization
Vector Databases and Embedding in practice
16 Topics
Topics
- What are Text Embeddings?
- How LLMs and Embedding Models generate embeddings
- Semantic Similarity and Vector Space
- Introduction to Vector Databases
- Key features: Indexing, Metadata Filtering, CRUD operations
- ChromaDB: Local setup, Collections, Document and Embedding Storage
- Pinecone: Cloud-native, Indexes, Namespaces, and Metadata filtering
- Weaviate: Open-source, Vector-native, and Graph Capabilities
- Other Vector Databases: FAISS, Milvus, Qdrant
- Similarity Search Algorithms
- Building Search Pipelines End to End with an Example Code
- Vector Indexing techniques
- Data Modeling in Vector Databases
- Updating and Deleting Vectors
- Choosing the Right Embedding Model
- Evaluation of Retrieval quality from Vector Databases
![skill]( "Hands On Experience")
- Building a Search Pipeline
- Retrieval Evaluation
![skill]( "skill you will learn")
- Text Embedding Concepts
- Vector Database Usage
- Similarity Search
Building and Deploying End-to-End GenAI Applications
12 Topics
Topics
- Architecting LLM-Powered Applications
- Types of GenAI Apps: Chatbots, Copilots, Semantic Search / RAG Engines
- Design Patterns: In-Context Learning vs RAG vs Tool-Use Agents
- Stateless vs Stateful Agents
- Modular Components: Embeddings, VectorDB, LLM, UI
- Key Architectural Considerations: Latency, Cost, Privacy, Memory, Scalability
- Building GenAI APIs with FastAPI
- RESTful Endpoint Structure
- Async vs Sync, CORS, Rate Limiting, API Security
- Orchestration Tools: LangServe, Chainlit, Flowise
- Cloud Deployment: GCP
- Containerization and Environment Setup
![skill]( "Hands On Experience")
- Wrap LLM into FastAPI
- Deploy Chatbot using LangChain
- GCP Cloud Run Deployment
- Logging with LangSmith
![skill]( "skill you will learn")
- GenAI App Design
- REST API Development
- Cloud Deployment
Evaluating GenAI Applications and Enterprise Use Cases
12 Topics
Topics
- Evaluation Metrics: Faithfulness, Factuality, RAGAs, BLEU, ROUGE, MRR
- Human and Automated Evaluation Loops
- Logging, Tracing, and Observability Tools: LangSmith, PromptLayer, Arize
- Prompt and Output Versioning
- Chain Tracing and Failure Monitoring
- Real-Time Feedback Collection
- GenAI Use Cases: Customer Support, Legal, Healthcare, Retail, Finance
- Contract Summarization
- Legal Q&A Bots
- Invoice Parsing with RAG
- Product Search Applications
- Domain Adaptation Strategies
![skill]( "Hands On Experience")
- Calculate RAGAs metrics for retrieval faithfulness.
- Set up LangSmith for real-time feedback collection
![skill]( "skill you will learn")
- Evaluating and monitoring GenAI model performance
- Implementing effective observability and debugging workflows
Multimodal LLMs and Beyond
14 Topics
Topics
- Introduction to Multimodal LLMs (GPT-4V, LLaVA, Gemini)
- How multimodal models process different data types
- Use Cases: Image Captioning, Visual Q&A, Video Summarization
- Working with Vision-Language Models (VLMs): Image inputs, text outputs
- Image Loaders in LangChain/LlamaIndex
- Simple visual Q&A applications
- Audio Processing with LLMs: Speech-to-Text (ASR)
- Text-to-Speech (TTS) integration
- Video understanding with LLMs
- Challenges in Multimodal AI
- Ethical Considerations in Multimodal AI
- Agent Frameworks (AutoGPT, CrewAI, LangGraph, MetaGPT)
- ReAct and Plan-and-Act agent strategies
- Future Directions
![skill]( "Hands On Experience")
- Build visual Q&A pipelines
![skill]( "skill you will learn")
- Multimodal Understanding
- Vision-Language Processing
- Agent Frameworks
Bonus Module: Fine-tuning & PEFT (Self-paced)
12 Topics
Topics
- Introduction to LLMOps: Managing the ML Lifecycle for Large Language Models
- Prompt Versioning and Experiment Tracking
- Model Monitoring: Latency, Drift, Failures, and Groundedness
- Safety and Reliability Evaluation: Toxicity, Hallucination, Bias Detection
- Evaluation Frameworks Overview: RAGAS, TruLens, LangSmith
- RAG Evaluation with RAGAS: Precision, Recall, Faithfulness
- Observability in Production: Logs, Metrics, Tracing LLM Workflows
- Using LangSmith for Chain/Agent Tracing, Feedback, and Dataset Runs
- Integrating TruLens for Human + Automated Feedback Collection
- Inference Cost Estimation and Optimization Techniques
- Budgeting Strategies for Token Usage, API Calls, and Resource Allocation
- Production Best Practices: Deploying With Guardrails and Evaluation Loops
![skill]( "Hands On Experience")
- Fine-tune a small LLM using LoRA with the PEFT library on Google Colab
- Apply QLoRA to a quantized model using Hugging Face + Colab setup
- Implement adapter tuning on a pre-trained model for a classification task
- Compare output quality before and after finetuning using evaluation prompts
![skill]( "skill you will learn")
- Finetuning LLMs with LoRA, QLoRA, and Adapters
- Selecting optimal finetuning techniques for different scenarios
- Setting up and running parameter-efficient finetuning workflows using Hugging Face
Bonus Module: LLMOps and Evaluation (Self-paced)
12 Topics
Topics
- Introduction to Model Finetuning: When Prompt Engineering Isnโt Enough
- Overview of Parameter-Efficient Finetuning (PEFT)
- LoRA (Low-Rank Adaptation): Concept and Architecture
- QLoRA: Quantized LoRA for Finetuning Large Models Efficiently
- Adapter Tuning: Modular and Lightweight Finetuning
- Comparing Finetuning Techniques: Full vs. LoRA vs. QLoRA vs. Adapters
- Selecting the Right Finetuning Strategy Based on Task and Resources
- Introduction to Hugging Face Transformers and PEFT Library
- Setting Up a Finetuning Environment with Google Colab
- Preparing Custom Datasets for Instruction Tuning and Task Adaptation
- Monitoring Training Metrics and Evaluating Fine-tuned Models
- Use Cases: Domain Adaptation, Instruction Tuning, Sentiment Customization
![skill]( "Hands On Experience")
- Track and compare multiple prompt versions using LangSmith
- Implement a RAG evaluation pipeline using RAGAS on a custom QA system
- Monitor model behavior and safety using TruLens in a live demo
- Visualize cost and performance metrics from a deployed LLM API
![skill]( "skill you will learn")
- Setting up LLMOps pipelines for observability and evaluation
- Using RAGAS, TruLens, and LangSmith to assess model quality and safety
- Managing cost and performance trade-offs in production GenAI systems
Prompt Engineering with LLM Course Details
About Prompt Engineering with LLM Course
This course covers basic to advanced generative AI techniques like prompt engineering, retrieval-augmented generation (RAG), and vector databases, preparing you to design and deploy cutting-edge GenAI applications. Learn to use tools such as Python, PyTorch, LangChain, OpenAI, and others while mastering LLM APIs, application architecture, and production-ready deployments.
What is LLM?
A large language model (LLM) is a type of machine learning model designed to understand and generate human language using neural networks with millions of parameters, trained on vast text corpora through self-supervised learning. LLMs power applications like chatbots, translation, summarization, and coding. Advanced models like GPTs are enhanced through fine-tuning and prompt engineering.
Why learn LLM?
Learning Large Language Models (LLMs) empowers you with the skills to build AI-powered solutions that understand and generate human language. As enterprises rapidly adopt these models for automation, content creation, and decision-making, professionals with LLM expertise are in high demand. Mastering LLMs and prompt engineering opens doors to roles like AI prompt engineer, GenAI engineer, and LLM engineer in todayโs evolving tech landscape.
What are the examples of LLM?
Large Language Models (LLMs) include well-known examples like GPT-4 by OpenAI, Gemini by Google, Claude by Anthropic, LLaMA by Meta, and Mistral. These models process vast amounts of text to perform tasks such as writing, summarizing, translating, and engaging in conversations. Each model has unique strengths, making them valuable across various industries and applications.
How is LLM different from generative AI??
Large Language Models (LLMs) are a type of generative AI specialized in understanding and producing human language. Generative AI is a broader category that includes models for text, images, audio, and more.
While all LLMs are generative AI, not all generative AI models are LLMs. LLMs focus on language tasks, whereas generative AI can create diverse types of content across multiple formats.
Is it worth learning Prompt Engineering with LLM?
Yes, learning Prompt Engineering with LLM is highly worth it. Demand for prompt engineers is rapidly rising across industries, with roles offering strong salaries and career growth as businesses adopt AI-driven solutions using LLMs. This skillset is essential for creating effective, reliable AI outputs and opens doors to diverse, future-ready job opportunities.
What are the prerequisites for this Prompt Engineering with LLM Course?
In order to complete this course successfully, participants need to have a basic understanding of Python programming language, machine learning, deep learning, natural language processing, generative AI, and prompt engineering concepts. However, learners will be provided with self-learning refresher material on generative AI and prompt before beginning with this live classes.
Why should you become an LLM Engineer?
Becoming an LLM Engineer is a highly rewarding career choice due to the rapidly growing demand, the potential to shape the future of AI, and the opportunity to work on cutting-edge technologies. The field offers a blend of technical and creative challenges, making it intellectually stimulating and engaging.
What will participants learn during the Prompt Engineering with LLM Course?
Participants will learn how to create and optimize prompts for various NLP tasks using techniques like zero-shot, one-shot, and few-shot prompting. They will develop skills in prompt testing, debugging, and evaluating model responses to improve prompt effectiveness through practical hands-on exercises.
Who should take this Prompt Engineering with LLM Course?
The Prompt Engineering with LLM Course is ideal for AI enthusiasts, developers, and professionals interested in natural language processing, AI product development, or automation who want to improve their skills in crafting effective prompts to enhance AI-driven applications.
Is LLM the future??
Definitely, LLMs are emerging as a powerful force in AI development, driving innovation in communication, content generation, and workflow automation, despite ongoing refinements and limitations.
Is Gen AI Engineer a good Career Option?
Yes, becoming a Generative AI Engineer is an excellent career choice. The generative AI market is projected to exceed $1 trillion by 2034, growing at over 44% CAGR. This rapid growth drives strong demand and competitive salaries across industries. With skills in LLMs and prompt engineering, you can lead innovation and develop impactful AI-driven solutions.
How will I execute the practicals in this Prompt Engineering with LLM Course?
Practicals for this Prompt engineering course will be implemented using Python, VS Code, and Jupyter Notebook. A step-by-step guide for installation will be provided in the Learning Management System (LMS).
Edureka's Support Team will be available 24/7 to assist you in case you have any questions or face any technical issues during the practicals.
What is Prompt Engineering?โ
Prompt engineering involves optimizing artificial intelligence engineering for multiple purposes. It includes refining large language models (LLMs) using specific prompts and recommended outputs. Additionally, it focuses on enhancing input to different generative AI services to make text or images. With advancements in generative AI tools, prompt engineering becomes crucial for generating diverse content, such as robotic process automation bots, 3D assets, scripts, robot instructions, and various digital artifacts.
What are the system requirements for this Prompt Engineering with LLM Course?
The system requirements for this Prompt Engineering with LLM Course include:
- A laptop or desktop computer with a minimum of 8 GB RAM with Intel Core-i3 and above processor to run NLP and machine learning models is required.
- A stable and high-speed internet connection is necessary for accessing online course materials, videos, and software.
Prompt Engineering with LLM Course Projects
Automated Code Review Assistant
Design an AI-powered assistant that analyzes code snippets, offers improvement suggestions, and educates developers on coding best practices to enhance productivity.Document-Based Knowledge Assistant
Develop a Retrieval-Augmented Generation (RAG) system that efficiently retrieves and generates precise answers from extensive document collections in response to user queries.Financial Report Analyzer
Build a chatbot that summarizes and answers questions from financial statements and investor reports.Conversational API-Integrated Bot
Build a chatbot capable of interfacing with external APIs to deliver dynamic, real-time responses for applications such as customer support.Technical Troubleshooting Q&A System with Document Retrieval
Develop an AI-powered Q&A system that retrieves and analyzes information from technical guides and documentation to deliver precise solutions for IT and software troubleshooting ....Prompt Engineering with LLM Course Certification
Upon successful completion of the Prompt Engineering with LLM Course, Edureka provides the course completion certificate, which is valid for a lifetime.
To unlock Edurekaโs Prompt Engineering with LLM course completion certificate, you must ensure the following:
- Fully participate in this Prompt Engineering with LLM Course and complete all modules.
- Successfully complete the quizzes and hands-on projects listed in the curriculum.
The Prompt Engineering with LLM Course enhances your professional profile by validating your expertise in large language models and advanced prompt techniques. It demonstrates your understanding of LLM architectures, ethical considerations, and hands-on skills in crafting effective prompts and deploying AI solutions. This course strengthens your credibility and marketability, opening doors to high-impact roles in AI development and innovation across various industries.
After earning the Prompt Engineering with LLM Certification, you can pursue roles such as AI Prompt Engineer, AI/ML Engineer, Generative AI Engineer, LLM Engineer, NLP Engineer, AI Solution Architect, AI Researcher, and AI Product Manager. This certification enhances career growth in AI development, automation, and intelligent decision-making systems.
The Prompt Engineering with LLM certification can be challenging as it requires a strong grasp of large language models, prompt design strategies, and practical implementation skills. However, with consistent effort, hands-on practice, and a well-structured learning path, it is definitely attainable. Joining a course with real-world projects and expert mentorship can greatly improve your understanding and confidence in applying prompt engineering effectively.
Yes, once you complete the certification, you will have lifetime access to the course materials. You can revisit the course content anytime, even after completing the certification.
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Prompt Engineering with LLM Training Course FAQs
Is ChatGPT LLM or NLP?
An LLM bot, such as Chat GPT, is a specific type of NLP model that leverages deep learning techniques to process and generate human-like text. These models are trained on massive datasets and have billions of parameters, allowing them to generate coherent and contextually relevant text based on a given input.
What are the benefits of Large Language Models?
Large Language Models (LLMs) offer numerous benefits, such as delivering personalized recommendations, improving accessibility through language support and assistive tools, enhancing research by analyzing large datasets, boosting creativity in content and code generation, increasing efficiency by automating repetitive tasks, and reducing operational costs and human errors across various industries.
How Can Prompt Engineering Be Applied to Work Effectively with Large Language Models like ChatGPT?
Prompt engineering with ChatGPT helps to control and refine the outputs of large language models by tailoring the input queries. By learning how to design precise prompts, users can guide ChatGPT to provide more accurate and useful answers in various contexts, from writing content and solving technical problems to brainstorming ideas.
Why should I enroll in this best Prompt Engineering with LLM training online?
Enrolling in this Prompt Engineering with LLM training online places you at the cutting edge of AI innovation, equipping you with essential skills to design and optimize large language model prompts for real-world applications. As LLMs transform industries, this hands-on course offers expert-led guidance, practical projects, and lifetime access. Mastering prompt engineering unlocks opportunities in AI development, NLP, automation, and many high-growth sectors.
What if I miss a live class of this Prompt Engineering with LLM training course?
You will have access to recorded sessions that you can review at your convenience.
What if I have queries after I complete this Prompt Engineering with LLM training online?
You can reach out to Edurekaโs support team for any queries and youโll have access to the community forums for ongoing help.
What skills will I acquire upon completing the Prompt Engineering with LLM training course?
Upon completing the Prompt Engineering with LLM training, you will acquire skills in prompt structuring, prompt tuning, task-specific prompting, and model behavior analysis.
Who are the instructors for the Prompt Engineering with LLM Course?
All the instructors at edureka are practitioners from the Industry with minimum 10-12 yrs of relevant IT experience. They are subject matter experts and are trained by edureka for providing an awesome learning experience to the participants.
What programming languages, tools, and frameworks are most relevant for preparing for the Prompt Engineering with LLM certification?
For the Prompt Engineering with LLM certification, key tools and frameworks include OpenAI Playground for testing prompts and LangChain for building prompt workflows. Programming skills in Python, especially for prompt crafting and debugging, are essential. Familiarity with NLP tasks and prompt evaluation techniques is also important.
Can someone with minimal AI experience pursue the Prompt Engineering with LLM certification and find related job opportunities?
Yes, individuals with minimal AI experience can pursue the Prompt Engineering with LLM certification. A basic understanding of AI concepts and Python programming is helpful. The course builds skills in prompt design, tuning, and evaluation, enabling career opportunities in roles like AI Prompt Engineer, Gen AI Engineer, and LLM Engineer.
Will I get placement assistance after completing this Prompt Engineering with LLM training?
Edureka provides placement assistance by connecting you with potential employers and helping with resume building and interview preparation
How soon after signing up would I get access to the learning content?
Once you sign up, you will get immediate access to the course materials and resources.
Is the course material accessible to the students even after the Prompt Engineering with LLM training is over?
Yes, you will have lifetime access to the course material and resources, including updates.
What is the application of Large Language Models?
Large Language Models (LLMs) have a wide range of applications, including chatbots, content generation, language translation, text summarization, code generation, and customer support across various industries.
Is Bert a Large Language Model?
Yes, BERT is a Large Language Model developed by Google. It is designed for understanding the context of words in search queries and natural language tasks. BERT is primarily used for tasks like text classification and question answering. GPT, developed by OpenAI, is another prominent example of a Large Language Model, but it focuses more on text generation.
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