Prompt Engineering Certification Course with LLM

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Edureka’s Prompt Engineering Course teaches you how to design, refine, and optimize prompts for advanced LLMs and generative AI systems. Through hands-on tasks, real-world projects, and industry tools, you'll gain skills in prompt patterns, chatbot prompting, AI workflow automation, NLP, and LLM evaluation, preparing you for career.

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Instructor-led Prompt Enginerring with LLM live online Training Schedule

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Why enroll for Prompt Engineering with LLM Training Course?

The Global LLM Market, valued at USD 7.77 billion in 2025, is projected to reach USD 123.09 billion by 2034 - Precedence Research

2,000+ Generative AI Engineer and LLM-related job openings worldwide, reflecting strong global demand for GenAI and LLM talent – LinkedIn.

The average annual salary for an AI Prompt Engineer in the US is US$136,000 with an average annual bonus of $37,000 - Glassdoor

Key Benefits of the Prompt Engineering Certification

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

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 You Will Learn in the Prompt Engineering Course

Generative AI Techniques
Prompt Engineering
Retrieval-Augmented Generation
Vector Database Management
Large Language Models
GenAI Application Development

Tools & LLM Platforms Covered in This Course

Prompt Engineering Course with LLM Curriculum

Curriculum Designed by Experts

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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

Hands-on

Creating a Small Transformer using PyTorch
Explore OpenAI Playground to test text generation

Skills

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

Hands-on

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

Skills

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

Hands-on

Implementing CoT and ToT
Testing Prompt Robustness
Auto-Prompt Generation
Human-in-the-loop Refinement

Skills

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

Hands-on

API Calls with OpenAI, Gemini, Anthropic
Tuning Parameters for Text Generation
Token Usage Optimization

Skills

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

Hands-on

Building Chains and Agents
Indexing with LlamaIndex
External API Integration
Observability Implementation

Skills

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

Hands-on:

Build a RAG Pipeline
Implement RetrievalQA With Custom Prompts
Evaluate Retrieval and Generation Quality Using Standard Metrics

Skills

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

Hands-on

Building a Search Pipeline
Retrieval Evaluation

Skills

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

Hands-on

Wrap LLM into FastAPI
Deploy Chatbot using LangChain
GCP Cloud Run Deployment
Logging with LangSmith

Skills

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

Hands-on

Calculate RAGAs metrics for retrieval faithfulness.
Set up LangSmith for real-time feedback collection

Skills

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

Hands-on

Build visual Q&A pipelines

Skills

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

Hands-on

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

Skills

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 Isn’t 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

Hands-on

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

Skills

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

Course Details: Key Features, Prerequisites & System Requirements

Prompt Engineering Course Overview and Key Features

This LLM Prompt Engineering Certification Course guides you through basic to advanced generative AI techniques, including prompt engineering, retrieval-augmented generation (RAG), and vector databases. You will gain practical skills to design and deploy cutting-edge GenAI applications using popular tools such as Python, PyTorch, LangChain, and OpenAI. The course also focuses on mastering LLM APIs, application architecture, and production-ready deployment strategies, equipping you to build real-world AI solutions.

Keyfeatures

Comprehensive coverage from fundamentals to advanced generative AI concepts
Hands-on experience with prompt crafting techniques to elicit precise LLM responses
Exploration of advanced prompting strategies like zero-shot, few-shot, and chain-of-thought prompting
Training on retrieval-augmented generation (RAG) and vector database integration
Practical usage of key tools and libraries: Python, PyTorch, LangChain, OpenAI API, and more
Understanding of LLMOps principles for deploying and managing LLM applications
Insights into ethical considerations, including bias and misinformation in prompt design
Application-focused learning across diverse domains such as content creation, code generation, and data analysis

Who should take this LLM prompt engineering certification course?

If you are an AI enthusiast, developer, or professional working with natural language processing, AI product development, or automation and you want to get better at designing effective prompts to make your AI applications smarter, the Prompt Engineering with LLM Course is a great fit for you.

What are the prerequisites for this course?

To succeed in this course, you should have a basic understanding of Python, machine learning, deep learning, and natural language processing.

What are the system requirements for the LLM Prompt Engineering 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.

How do I execute the practicals in this course?

Practical for this Prompt Engineering course are done using Python, VS Code, and Jupyter Notebook. You will get a detailed step-by-step installation guide in the LMS to set up your environment smoothly.

Real-World Prompt Engineering Projects You Will Build

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.

View Project Details

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.

View Project Details

Financial Report Analyzer

Build a chatbot that summarizes and answers questions from financial statements and investor reports.

View Project Details

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.

View Project Details

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 ....

View Project Details

Prompt Engineering Certification Details

Upon successful completion of the Prompt Engineering with LLM Course, Edureka provides the course completion certificate, which is valid for a lifetime.

To unlock Edureka’s Prompt Engineering with LLM course completion certificate, you need to fully participate in the course by completing all the modules and successfully finish the quizzes and hands-on projects included in the curriculum.

The Prompt Engineering with LLM certification can be tough if you’re new to the field,it covers a lot, from understanding how large language models work to actually crafting prompts and building projects.

John Doe

Title

with Grade X

XYZ123431st Jul 2024

The Certificate ID can be verified at www.edureka.co/verify to check the authenticity of this certificate

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reviews

Read learner testimonials

Lipsa SahooQuality Assurance Engineer,On Solve

★★★★★

I recently purchased the Prompt Engineering course at Edureka. The course content was well organised, the instructor was very knowledgeable and answered all the queries. Also the class recordings are available after each class completion ,which helps to revise the topics again and go through missed lectures.

April 09, 2026

Sunil KumarAssociate Director,LTI Mindtree

★★★★★

I have recently completed Prompt Engineering with Generative AI course in Edurekha and I recommend everyone who wants to upgrade their skill to go for it. Trainer and Training material is very informative, knowledgeable and has good insights. Even the certification is based on Project submission unlike other certifications courses wherein you need to answer questions which I felt is very apt and all Training organaizations should implement it. Above all this training is worth for the money I paid.

April 10, 2026

Michael HarkinsSolution Engineer-Open Source Analytics at IBM, former Systems Architect at Hortonworks

★★★★★

The courses are top rate. The best part is live instruction, with playback. You get all the presentations and labs. Great instructions. But my favorite feature is viewing a previous class. They provide a set of videos from a previous session, so you can watch the course before you participate. This way you can get the most out of the course. Also, they are always there to answer questions, and prompt when you open an issue if you are having any trouble. Added bonus ~ you get lifetime access to the course you took!!! I have taken so many courses and then not really gotten to work with a technology until I forgot most of what was taught. Edureka lets you go back later, when your boss says I want this ASAP!" ~ This is the killer education app... I've take two courses and I'm taking two more. Love these guys."

December 09, 2017

Karunakar ReddySenior System Analyst at Santander UK

★★★★☆

Edureka has very good instructors and technical support team, I have completed the course BIG DATA & AWS through web training, and it was very good training, helped me to learn very swiftly. When I stuck up in installation and in writing map reduce programming technical support resolved very quickly and I was very happy with their response in time. As well as they providing life time access for our completed course videos so it will be useful in real time projects. I feel very happy to recommend Edureka for my friends on big data course.

December 09, 2017

Vijay MajetiFunctional Data Analyst at Yorkshire Building Society Group

★★★★★

Very good learning experience. Training staff are really experienced. 24X7 Support is excellent,get response instantly. You can learn new advanced technologies from home with real-time scenarios. Easy for IT professionals to shift technologies. Great Edureka!!!

December 09, 2017

Rahul KushwahDevops Software Developer and AWS Certified Solutions Architect

★★★★★

Edureka is BEST in provide e-learning courses for all software programs including latest technologies. I have attended Devops Course and i leant alot for it. They have good instructors. They are well structured and provided both ease of access and depth while allowing you to go at your own pace.Support team people is also really very cooperative and helps out at there best.

December 09, 2017

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Frequently Asked Questions : Prompt Engineering Course

What is LLM?

Large Language Model (LLM) is an AI trained on huge text data to understand and generate human-like language.

What is LLM-based prompt engineering?

Prompt engineering is the process of designing instructions that guide Large Language Models (LLMs) to produce accurate and useful outputs.

Why should I learn LLM?

Learning LLMs lets you build advanced AI apps like chatbots and content tools shaping the future of tech.

What are examples of LLMs?

Examples include OpenAI’s GPT series (ChatGPT, GPT-4), Google’s BERT,powerful models for language tasks.

What if I miss a live class of this training course?

You will have access to the recorded sessions that you can review at your convenience.

What if I have queries after I complete the course?

You can reach out to Edureka’s support team for any queries and you’ll 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 LLM Prompt Engineering 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 is the salary of a prompt engineer fresher?

According to Glassdoor, Freshers in India typically start around ₹₹6 to ₹7 LPA, while in the US it can range from $70,000 to $100,000 annually.

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 there a demand for prompt engineering?

Yes, prompt engineers are currently in high demand. With the rapid growth of AI adoption, companies are actively seeking professionals skilled in prompt design.