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

What is prompt engineering in LLM?

It's the art of crafting inputs (prompts) that guide large language models (LLMs) to give accurate, useful responses.

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 cost of a prompt engineering course?

The price of the course is 18,999 INR.

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.

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.

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.

Is prompt engineering the future?

Its future is more about growing and adapting than becoming outdated.

LLM Prompt Engineering Certification Course Online

Name: LLM Prompt Engineering Certification Course
Item: LLM Prompt Engineering Certification Course
Rating: 5
Author: Muralidhar Gaddam

Why should you take 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

Generative AI Essentials

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 you will learn:

Generative AI Fundamentals
Transformer Architecture
LLM Training Process
Responsible AI Practices

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Prompt Engineering Essentials

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 you will Learn:

Prompt Structuring
Prompt Tuning
Task-Specific Prompting
Model Behavior Analysis

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Advanced Prompting Techniques

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 you will Learn:

Multi-step Prompting
Prompt Injection Defense
Semantic Prompt Optimization
Prompt Evaluation Techniques

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Working with LLM APIs and SDKs

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 you will Learn:

API Integration
Parameter Tuning
Inference Techniques
Cost Optimization

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Building LLM Apps with LangChain and LlamaIndex

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 you will Learn:

LangChain Workflows
Document Indexing
Tool Integration

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Developing RAG Systems

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 you will Learn:

AG Architecture Understanding
Document Retrieval Techniques
Prompt Customization

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Vector Databases and Embedding in practice

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 you will Learn:

Text Embedding Concepts
Vector Database Usage
Similarity Search

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Building and Deploying End-to-End GenAI Applications

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 you will Learn:

GenAI App Design
REST API Development
Cloud Deployment

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Evaluating GenAI Applications and Enterprise Use Cases

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 you will Learn:

Evaluating and monitoring GenAI model performance
Implementing effective observability and debugging workflows

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Multimodal LLMs and Beyond

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 you will Learn:

Multimodal Understanding
Vision-Language Processing
Agent Frameworks

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Bonus Module: Fine-tuning & PEFT (Self-paced)

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 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

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Bonus Module: LLMOps and Evaluation (Self-paced)

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 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

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View course curriculum

Testimonial Reviews

GopinathSAP Architect Consultant, IBM India Pvt Ltd, Bengaluru, Karnataka, India

I attended the demo session without any intention of joining a course. But the demo class was so imp ... Read More

Puneet Jhajj

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Muralidhar GaddamDevOps Professional, Project/Program Management Corporate Trainer

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LLM Prompt Engineering Certification Course

Why should you take Prompt Engineering with LLM Training Course?

Instructor-led Prompt Engineering with LLM Training Course live online classes

Financing Options

Curriculum

Generative AI Essentials

Prompt Engineering Essentials

Advanced Prompting Techniques

Working with LLM APIs and SDKs

Building LLM Apps with LangChain and LlamaIndex

Developing RAG Systems

Vector Databases and Embedding in practice

Building and Deploying End-to-End GenAI Applications

Evaluating GenAI Applications and Enterprise Use Cases

Multimodal LLMs and Beyond

Bonus Module: Fine-tuning & PEFT (Self-paced)

Bonus Module: LLMOps and Evaluation (Self-paced)

Prompt Engineering Course Details

Prompt Engineering Course Overview and Key Features

Who should take this LLM prompt engineering certification course?

What are the prerequisites for this course?

What are the system requirements for the LLM Prompt Engineering course?

How do I execute the practicals in this course?

Testimonial Reviews

Video Reviews

Prompt Engineering Course FAQs

What is LLM?

What is prompt engineering in LLM?

Why should I learn LLM?

What are examples of LLMs?

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

What if I have queries after I complete the course?

What skills will I acquire upon completing the Prompt Engineering with LLM training course?

Who are the instructors for the LLM Prompt Engineering Course?

What is the cost of a prompt engineering course?

What is the salary of a prompt engineer fresher?

Will I get placement assistance after completing this Prompt Engineering with LLM training?

How soon after signing up would I get access to the learning content?

Is the course material accessible to the students even after the Prompt Engineering with LLM training is over?

Is there a demand for prompt engineering?

Is prompt engineering the future?

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