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LLM Prompt Engineering Certification Course Online

LLM Prompt Engineering Certification Course Online
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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?

    pay scale by Edureka courseThe Global LLM Market, valued at USD 7.77 billion in 2025, is projected to reach USD 123.09 billion by 2034 - Precedence Research
    Industries2,000+ Generative AI Engineer and LLM-related job openings worldwide, reflecting strong global demand for GenAI and LLM talent โ€“ LinkedIn.
    Average Salary growth by Edureka courseThe average annual salary for an AI Prompt Engineer in the US is US$136,000 with an average annual bonus of $37,000 - Glassdoor

    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.
    Annual Salary
    LLM Engineer average salary
    Hiring Companies
     Hiring Companies
    Annual Salary
    Generative AI Engineer average salary
    Hiring Companies
     Hiring Companies
    Annual Salary
    AI Prompt Engineer average salary
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    Why Prompt Engineering with LLM Training Course from edureka

    Live Interactive Learning

    Live Interactive Learning

    • World-Class Instructors
    • Expert-Led Mentoring Sessions
    • Instant doubt clearing
    Lifetime Access

    Lifetime Access

    • Course Access Never Expires
    • Free Access to Future Updates
    • Unlimited Access to Course Content
    24x7 Support

    24x7 Support

    • One-On-One Learning Assistance
    • Help Desk Support
    • Resolve Doubts in Real-time
    Hands-On Project Based Learning

    Hands-On Project Based Learning

    • Industry-Relevant Projects
    • Course Demo Dataset & Files
    • Quizzes & Assignments
    Industry Recognised Certification

    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

    • skillGenerative AI Techniques
    • skillPrompt Engineering
    • skillRetrieval-Augmented Generation
    • skillVector Database Management
    • skillLarge Language Models
    • skillGenAI Application Development

    Tools Covered

    • python
    • Jupyter
    • Visual Studio Code
    • colab
    •  pytorch
    • Hugging Face
    • Open AI
    • ANTHROPIC
    • LangChain
    • LlamaIndex
    • Chroma
    • Pinecone
    • Weaviate
    • Milvus
    • drant
    • FastAPI
    • Google cloud Platform
    • docker
    • LangSmith
    • crewai

    Curriculum

    Curriculum Designed by Experts

    AdobeIconDOWNLOAD CURRICULUM

    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

    skillHands-on

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

    skillSkills

    • 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

    skillHands-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

    skillSkills

    • 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

    skillHands-on

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

    skillSkills

    • 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

    skillHands-on

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

    skillSkills

    • 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

    skillHands-on

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

    skillSkills

    • 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

    skillHands-on:

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

    skillSkills

    • 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

    skillHands-on

    • Building a Search Pipeline
    • Retrieval Evaluation

    skillSkills

    • 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

    skillHands-on

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

    skillSkills

    • 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

    skillHands-on

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

    skillSkills

    • 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

    skillHands-on

    • Build visual Q&A pipelines

    skillSkills

    • 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

    skillHands-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

    skillSkills

    • 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 Isnโ€™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

    skillHands-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

    skillSkills

    • 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

    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, natural language processing, generative AI, and prompt engineering concepts. However, you will receive self-learning refresher materials on generative AI and prompt engineering before the live classes begin.

          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. Additionaly, Edurekaโ€™s Support Team is available 24/7 to help with any questions or technical issues during your practical sessions.

            Prompt Engineering with LLM Course Projects

             certification 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.
             certification projects

            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.
             certification projects

            Financial Report Analyzer

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

            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.
             certification projects

            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 Edurekaโ€™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 youโ€™re new to the field,it covers a lot, from understanding how large language models work to actually crafting prompts and building projects.
            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.

            Edureka Certification
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            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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            Definitely there is no doubt in saying that all the instructors at Edureka are industry experienced and the support staff provides a quick response to the tickets you log whether it be Day or Night. I like the way the sessions have been organized, with the Pre-requisites required for the next session and the assignments, QUIZ post session etc...I like the LMS a lot, You can find enough of required information in the forums. They even share the video recordings of other instructors as well in the LMS. So that if one couldn't get the content clearly in your session, you can always refer to other instructors recordings shared in your LMS. This part helped me in understanding few concepts in a better way.

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            FAQs

            What is LLM?

            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 OpenAIโ€™s GPT series (ChatGPT, GPT-4), Googleโ€™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 Edurekaโ€™s support team for any queries and youโ€™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 โ‚นโ‚น6 to โ‚น7 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.
            Have more questions?
            Course counsellors are available 24x7
            For Career Assistance :