Prompt Engineering with LLMs Course Online

Name: Prompt Engineering with LLMs Training Course
Item: Prompt Engineering with LLMs Training Course
Rating: 5
Author: Raghava Beeragudem

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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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 today’s 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?‎

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.

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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 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 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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Prompt Engineering with LLMs Training 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)

Course Details

About Prompt Engineering with LLM Course

What is LLM?

Why learn LLM?

What are the examples of LLM?

How is LLM different from generative AI??

Is it worth learning Prompt Engineering with LLM?

What are the prerequisites for this Prompt Engineering with LLM Course?

Why should you become an LLM Engineer?

What will participants learn during the Prompt Engineering with LLM Course?

Who should take this Prompt Engineering with LLM Course?

Is LLM the future??

Is Gen AI Engineer a good Career Option?

How will I execute the practicals in this Prompt Engineering with LLM Course?

What is Prompt Engineering?‎

What are the system requirements for this Prompt Engineering with LLM Course?

Testimonial Reviews

Video Reviews

FAQs

Is ChatGPT LLM or NLP?

What are the benefits of Large Language Models?

How Can Prompt Engineering Be Applied to Work Effectively with Large Language Models like ChatGPT?

Why should I enroll in this best Prompt Engineering with LLM training online?

What if I miss a live class of this Prompt Engineering with LLM training course?

What if I have queries after I complete this Prompt Engineering with LLM training online?

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

Who are the instructors for the Prompt Engineering with LLM Course?

What programming languages, tools, and frameworks are most relevant for preparing for the Prompt Engineering with LLM certification?

Can someone with minimal AI experience pursue the Prompt Engineering with LLM certification and find related job opportunities?

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?

What is the application of Large Language Models?

Is Bert a Large Language Model?

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