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🛡️ 🎓 From Zero to Tech Hero: Learn AI, GPTs, Prompt Context Engineering & Web Fundamentals — Fast, Fun, and Future-Ready
🤖 AI Concepts, Prompt + Context Engineering (GPT, LLMs). ⚙️ Agentic Systems & No-Code/Low-Code Workflow Automation 🌐 Web Development (Frontend, WordPress & Elementor )📊 Infographics & Visual Storytelling for Impact
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Turn your skills into income with hands-on projects, real clients, and expert mentorship.
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🔍 About the Prompt Engineering with LLM and Agentic AI Training Course
This course provides hands-on expertise in Prompt Engineering and Large Language Models (LLMs) like GPT, LLaMA, Claude, and Gemini. You'll learn to design effective prompts, optimize responses, and build end-to-end GenAI applications. It’s tailored for developers, analysts, and AI enthusiasts who want to build smart, conversational AI systems. - Covers theory + real-world labs. Includes Prompting, RAG, Agents, APIs, and LangChain. Practical use cases: Chatbots, Copilots, LLM-powered tools . Ideal for those working with AI, NLP, and automation.
A Large Language Model (LLM) is an AI model trained on massive amounts of text data to understand, generate, and manipulate human-like language. Examples include OpenAI’s GPT-4, Meta’s LLaMA, and Google’s Gemini. LLMs can power tasks like summarization, Q&A, coding, and creative writing.
Learns from trillions of words
Works across multiple languages and tasks
Supports plug-ins, tools, and API-based extensions
Core engine behind modern GenAI platforms
LLMs are transforming how we build apps, interact with machines, and automate workflows. Learning how to use and build with LLMs gives you a critical edge in tech, business, and content roles.
Future-proof your AI skillset
Understand ChatGPT, Claude, and open-source models
Build scalable and intelligent AI tools
Unlock career opportunities in GenAI
LLMs are used across industries to streamline work, assist customers, generate insights, and much more.
Chatbots for customer support and onboarding
Code generation for dev productivity
RAG systems for enterprise search
Language translation and summarization
LLMs are a subset of Generative AI. While GenAI also includes image, video, and audio generation, LLMs are focused on text-based tasks. Think of LLMs as the engine that drives GenAI text capabilities.
LLM = language-focused GenAI
GenAI includes multimodal (text + image + audio)
LLMs power ChatGPT, Copilot, Gemini, etc.
GenAI = LLMs + diffusion models + speech models
Absolutely. Prompting is the interface layer between users and LLMs. If you can write powerful prompts, you can shape how LLMs behave—without needing to retrain models.
In-demand across AI jobs
Core skill for GenAI product builders
Saves time and cost in model development
Essential for safety, alignment, and reliability
You don't need a PhD to take this course. Some coding experience (Python), curiosity, and willingness to learn are enough.
Basic understanding of Python and APIs
Familiarity with JSON, REST, and CLI tools
Interest in AI, NLP, or automation
Laptop with browser + internet for hands-on labs
LLM Engineers are among the highest-paid emerging tech roles. They sit at the intersection of AI, software development, and UX—designing intelligent agents and smart workflows.
High demand across sectors
Key role in GenAI app development
Contributes to AI safety and performance
Combines creativity, logic, and systems thinking
You’ll learn to build with and around LLMs, using prompt engineering, RAG, LangChain, APIs, and evaluation tools.
Design prompts: zero-shot, few-shot, CoT
Deploy LLM apps with FastAPI, Chainlit, LangServe
Work with vector DBs, embeddings, and RAG pipelines
Evaluate and optimize outputs using tools like RAGAS & LangSmith
Anyone interested in building AI tools using LLMs, regardless of background. It’s especially great for:
Developers and Data Engineers
Product Managers and AI Strategists
NLP/ML Professionals
Creators exploring automation with GenAI
Yes. LLMs are the foundation of many GenAI tools being deployed today. As they evolve to handle multimodal inputs and reasoning, their influence will only grow.
Core engine behind GenAI products
Increasingly integrated with tools and APIs
Becoming more personalized and domain-aware
Paving the way for cognitive automation
GenAI Engineering is one of the fastest-growing and well-paying fields today. Companies seek professionals who can build with LLMs, not just use them.
Roles: Prompt Engineer, LLM Developer, RAG Engineer
High salaries and career growth
Combines AI, software, and product thinking
Relevant across industries: healthcare, fintech, education, legal
You’ll use real tools like OpenAI Playground, Hugging Face, LangChain, and Colab notebooks. All labs are cloud-based with step-by-step instructions.
No special GPU required
Fully online hands-on exercises
Real project workflows using public APIs
GitHub repos, datasets, and Colab templates provided
It’s the skill of crafting effective instructions (prompts) to guide LLMs toward accurate, useful, and reliable outputs. It’s the UX layer of LLM systems.
Interface between humans and models
Involves structure, tone, context, and reasoning
Crucial for safety, task accuracy, and consistency
Low-code gateway to GenAI
You only need a modern browser, a stable internet connection, and optionally a free Google Colab or GitHub account.
Chrome/Firefox/Edge browser
Google account for Colab & Drive
GitHub for project code access
No local installation or GPU required
This course provides a comprehensive framework for building agent-based AI systems.
From zero-shot planning to memory-enhanced execution, gain practical experience in each module.
Designed for engineers, product teams, and AI innovators pushing the frontier of autonomy.
Build autonomous AI using LLMs, APIs, retrievers, and tool-chaining.
🔗 Covers LangChain Agents, ReAct, AutoGPT & CrewAI
🧩 Deep dives into Tool Use, Memory, and Planning
⚙️ Project-based labs for every concept
🧭 Learn Retrieval-Augmented Generation (RAG) with Agents
You don’t need to be an AI expert—but basic Python and LLM knowledge help.
This course assumes familiarity with prompts, APIs, and common AI concepts.
We’ll walk you through foundational to advanced agentic thinking with hands-on support.
Tech-savvy professionals will find it easy to follow.
🐍 Python knowledge recommended
🧠 Understanding of LLMs & Prompting helps
🔌 API basics required (OpenAI, LangChain, etc.)
🔍 Curious mindset and logic-oriented thinking encouraged
Gain the skills to design, build, and evaluate autonomous agents.
You’ll master key concepts like planning, memory, RAG, evaluation loops, and tool use.
From single-agent tasks to multi-agent collaboration, this course scales in complexity.
End with deployable AI systems ready for real-world integration.
🧠 Implement multi-agent systems with tools & retrievers
🛠 Build custom Agents using ReAct, Plan-And-Execute, etc.
🕸️ Understand workflows with memory and long-term context
📦 Package and deploy your agentic apps at scale
This course is for developers, AI engineers, product managers, and startups building with LLMs.
If you want to go beyond simple chatbots into autonomous AI, this is your course.
Researchers exploring AGI, or enterprises building AI copilots will benefit too.
Join if you want to lead the next wave of intelligent automation.
👩💻 AI/ML Engineers & Full-stack Developers
🧑🔬 Researchers in NLP, AGI, Cognitive AI
🚀 Startup Teams building agentic SaaS or copilots
🏢 Product Managers in AI-first organizations
Our pricing is built to support individual learners and enterprise teams.
Scholarships, group discounts, and institutional licenses are available.
You get full access to labs, source code, templates, and deployment scripts.
Upgrade your AI skills at an affordable price.
💰 One-time access: ₹ Pricing section (includes lifetime updates)
🧑🏫 Team license available with reporting dashboard
📂 30+ ready-to-use agent project templates
🎓 Scholarship support for students & freelancers
The course is structured into 14 intensive modules, each with practical labs.
It’s designed to be completed in 4 weeks with 6–8 hours/week commitment.
Self-paced access lets you learn flexibly while building agentic projects.
Complete the course at your speed—with lifetime access.
📅 Duration: 4 Weeks (Self-paced)
🧪 14 Modules + 28 Labs
🧑💻 Weekly live Q&A sessions with experts
📜 Certificate + Portfolio-ready Agent Projects
A Large Language Model (LLM) is an AI system that processes and generates human-like text from vast datasets.
It leverages deep learning to understand language context, intent, and nuance at scale.
LLMs power chatbots, translation engines, writing assistants, and more innovative applications.
They are the engines behind today’s conversational AI and generative content systems.
🧩 Advanced Neural Architectures – Built on transformers and attention mechanisms
📚 Massive Training Data – Trained on diverse, real-world language data
🔍 Contextual Understanding – Capable of grasping subtle language cues
💬 Text Generation – Produces natural, coherent, and creative outputs
Learning about LLMs is critical as they transform traditional computing into intelligent, conversational systems.
LLM expertise opens opportunities in software development, AI research, and digital product design.
These models are driving the next wave of innovation in language processing and human-computer interaction.
Understanding LLMs enables you to build smarter, context-aware applications and solutions.
🚀 Future-Proof Your Skills – Stay ahead in the rapidly evolving AI landscape
🎓 Unlock New Career Paths – From AI development to product innovation
🔧 Innovate with Technology – Create breakthrough applications in language AI
🌍 Impact Industries – Revolutionize sectors like customer support and education
Modern LLMs include groundbreaking models such as OpenAI’s GPT-4, Google’s Gemini, Meta’s LLaMA, and Anthropic’s Claude.
Each model brings unique strengths, from superior context retention to rapid inference speeds.
These models are applied in diverse fields, from creative content generation to technical problem-solving.
By studying these examples, you gain insight into best practices and innovative architectures for your projects.
🤖 GPT-4 & Claude – Leaders in natural language generation
🌟 Gemini & LLaMA – Cutting-edge alternatives with unique capabilities
⚡ Fast and Efficient – Optimized for speed and quality across tasks
🔄 Diverse Use Cases – From conversational agents to creative writing tools
LLMs are a subset of Generative AI focused specifically on text-based tasks and language understanding.
While Generative AI spans images, videos, and audio, LLMs specialize in generating human-like language responses.
This distinction is crucial: LLMs use language models to build applications like chatbots and text editors, whereas Generative AI covers a broader multimedia spectrum.
Understanding these differences lets you choose the right tool for your project’s creative and technical needs.
📝 Text-Centric – Specializes in natural language processing
🎨 Multimodal vs. Monomodal – Generative AI spans multiple media; LLMs focus on text
🔍 Deep Language Comprehension – Enables nuanced, context-aware outputs
🚀 Application-Specific – Drives text-based tools like chatbots and assistants
Absolutely—it’s a critical skill that enables you to directly control and enhance LLM outputs without retraining the model.
Prompt engineering bridges human creativity with machine intelligence, saving time and resources while delivering precise results.
This skill is in high demand across tech, business, and creative industries as companies strive to harness AI effectively.
Learning this art not only improves your AI applications but also positions you at the forefront of the GenAI revolution.
💎 High-Demand Skill – Sought after by leading tech companies
🔧 Cost-Effective Optimization – Enhances model performance without heavy retraining
🎯 Precision and Control – Fine-tunes AI output to meet specific needs
🌟 Competitive Advantage – A unique differentiator in your professional toolkit
Becoming an LLM Engineer empowers you to design, build, and fine-tune the intelligent systems that drive modern business.
It is a career that combines deep technical expertise with creative problem solving and strategic innovation.
As industries increasingly adopt AI, LLM Engineers are essential for bridging the gap between technology and user experience.
This role not only offers lucrative opportunities but also the chance to shape the future of human–machine interaction.
🔝 Lucrative Career Path – High earning potential and rapid industry growth
🚀 Tech Innovation Leader – Pioneering next-gen AI solutions
🛠 Hands-On Development – Design and optimize real-world applications
🌐 Broad Industry Impact – In demand in healthcare, finance, education, and more
Practicals are executed through interactive labs, cloud-based environments, and real-time coding exercises.
You will engage with popular frameworks and tools such as Hugging Face, LangChain, and Google Colab for hands-on learning.
Each practical session is designed to simulate real-world challenges and develop deployable solutions step by step.
Our guided projects ensure that you can apply theory to practice, track improvements, and produce portfolio-ready projects.
🖥️ Interactive Labs – Step-by-step projects on cloud platforms
📁 Real-World Tools – Hands-on use of Hugging Face, LangChain, and more
🔍 Guided Exercises – Detailed walkthroughs for each practical challenge
🎓 Portfolio Projects – Build real applications to showcase your skills
Discover the future of AI through this hands-on Agentic AI course.
You’ll learn how intelligent agents plan, act, and reason in dynamic environments.
Explore real-world use cases, build autonomous workflows, and develop intelligent systems.
This course bridges LLMs with tool-use, memory, reasoning, and multi-step autonomy.
🤖 Master AI agents with memory and tools
🔁 Automate multi-step workflows using LLMs
📊 Real-world projects with LangChain, ReAct, AutoGPT
🧠 Learn planning, chaining, reasoning, and feedback loops
Transparent Pricing
🔒 Secure Your Seat Before It’s Gone
Top FAQs Answered for FYKI
“Prompt engineering is the art and science of crafting inputs that guide AI models to produce optimal outputs.”
Prompt engineering is crucial for leveraging LLMs (Large Language Models) like GPT, Claude, or DeepSeek effectively. A well-designed prompt enhances performance, reduces errors, and controls the model's behavior. It is a fundamental skill in AI development and agent building.
Enables accurate and domain-aware outputs
Reduces hallucinations and vague responses
Core to agent design and fine-tuning LLMs
Essential for AI-driven automation and decision-making
“Prompting isn't just asking; it's teaching AI how to think contextually.”
Answer:
These are three core techniques in prompt design.
Zero-shot requires no examples, just instructions.
Few-shot includes a few example Q&A pairs.
In-context extends few-shot with prior conversation memory or documents.
Zero-shot = fastest, lowest cost, least guidance
Few-shot = more guidance, better structure
In-context = memory-aware, long-form reasoning
Critical for task-specific performance
“An AI agent is a self-operating workflow powered by LLMs that completes a goal using reasoning and tools.”
Answer:
Agents are advanced systems that combine prompts, memory, tools, and APIs to complete multi-step workflows.
They can schedule meetings, extract data, write reports, or act as customer service bots.
Built with LangChain, AutoGen, CrewAI, or Autogen Studio
Can access external tools, APIs, and files
Require memory, context window, and tool use planning
Foundation for AI automation and business tasks
“Automation isn’t just scripts anymore; it’s intelligent decisions by AI agents in real-time.”
Answer:
Workflow automation in GenAI uses AI agents to trigger actions based on conditions or input. It replaces manual steps with autonomous agents that can use reasoning, APIs, or internal tools.
Enables dynamic decision-making workflows
Integrates with CRMs, CMSs, and databases
Reduces time spent on repetitive tasks
Used in sales, support, HR, procurement, etc.
“From design to content, AI can now do it all — in seconds.”
Answer:
AI tools like Elementor AI, Canva AI, and Framer can design full websites and create stunning infographics with minimal input.
They support drag-and-drop, prompt-based UI generation, and responsive layouts.
Use prompts to generate site sections, copy, and forms
Visualize data with infographics using chart/graph AIs
Reduce cost and design cycles dramatically
Ideal for solopreneurs and small teams
“Your digital identity is your new passport for earning, networking, and growth in the AI age.”
Answer:
Digital Identity includes your website, portfolio, resume, social presence, and personal branding — all AI-powered. Tools like Copy.ai, Notion AI, and Resume.io can help.
Create AI-generated personal bios and resumes
Build personal landing pages using AI website builders
Automate profile content for LinkedIn, GitHub, Behance
Establish credibility and visibility in digital economy
“Monetize your skills through gigs, content, AI apps, or consultations — all starting from identity.”
Answer:
Once your digital profile is AI-optimized, you can earn through:
Freelance gigs (Fiverr, Upwork)
Selling digital products (Gumroad, Notion)
Building AI-based tools or services
Offering AI automation consulting
Build traffic with SEO-optimized AI blogs
Offer niche services (AI résumé writing, AI chatbot setup)
Sell templates or prompt packs
Use Gumroad, Ko-fi, or your own storefront
“You’ll go from basic prompt design to building domain-specific prompt systems.”
Answer:
The course covers prompt formats, types, memory prompts, chaining, and prompt optimization. It also includes testing and performance tuning.
Zero-shot, few-shot, chain-of-thought, RAG prompts
Prompt tuning vs. instruction tuning
Multi-modal prompting (text, image)
Prompt performance evaluation using RAGAS
“You’ll build real agents using open-source stacks and workflows.”
Answer:
The tools include LangChain, CrewAI, AutoGen, and LangGraph for chaining logic and building agent flows. Integration with FastAPI, Zapier, and APIs is also taught.
LangChain + LangSmith for traceable agents
AutoGen Studio for multi-agent planning
Pinecone/Weaviate for vector search
Zapier + Make for automation triggers
“Yes, AI makes it seamless to build multilingual, culturally-adaptive websites.”
Answer:
With tools like DeepL, GPT-4, and Elementor AI, you can build multilingual websites for global audiences.
Auto-translate bios, blogs, and UI text
Generate content in Hindi, Kannada, Tamil, etc.
Build language-specific landing pages
Rank better in local search with native content
Top FAQs Answered for FYKI
“You’ll be building monetizable projects in Week 1 itself.”
Answer:
Each module comes with hands-on mini-projects designed for real-world use cases: lead gen bots, portfolio sites, resume generators, etc.
Projects = Direct use for freelancing or clients
Prompt Packs = Sellable assets
Resume + Digital Brand = Profile visibility boost
Workflow Agents = Billable automation services
“Canva AI, Framer AI, and Elementor Pro are your new design stack.”
Answer:
These tools simplify the process of wireframing, designing, and launching websites with minimal effort and no coding.
Elementor Pro + AI for WordPress builders
Framer AI for modern SaaS-like UI
Midjourney or DALL·E for hero illustrations
Use ChatGPT to generate marketing copy
“Your prompt portfolio is proof that you can control the model — not just use it.”
Answer:
A prompt portfolio shows your skill in designing and optimizing prompts across domains like marketing, customer support, and technical writing.
Show before/after outputs
Annotate prompt decisions (e.g., format, modifiers)
Use screenshots or Loom videos
Host it on Notion, Framer, or GitHub
“Good prompts are measured by relevance, precision, and robustness.”
Answer:
Use tools like RAGAS, Prompt Layer, or LangSmith to track outputs. Metrics like token use, coherence, context retention, and accuracy are key.
RAGAS = Retrieval-Augmented Generation Assessment
LangSmith = Trace chains and prompt steps
Check Factual accuracy + hallucination rate
Evaluate across multiple edge cases
“AI isn’t just content — it’s creative direction too.”
Answer:
Yes, using tools like Looka, Brandmark, Canva AI, and NameLix, you can generate full branding assets.
Generate logo, favicon, and brandbook
Create brand voice with GPT
Build slogan/tagline options using prompts
Use Palette Generator for colors and fonts
“Every project is a portfolio and income opportunity.”
Answer:
Projects are designed around real-world deliverables — like creating a landing page, chatbot, prompt library, or automation flow.
Resume Generator with ChatGPT + Notion
Workflow Bot using LangChain + FastAPI
Personal Website using Elementor AI
Freelance Service Portal with Webflow AI
“If you can prompt ChatGPT, you can start here.”
Answer:
This course is ideal for freelancers, students, small business owners, and professionals in sales, marketing, design, and tech support.
No coding skills required for the basic track
Business/agency owners looking to automate
Freelancers wanting to earn with AI
Students aiming to build AI-powered portfolios
“Prompting, agenting, and personal branding are timeless AI skills.”
Answer:
Yes. The course evolves with models, and core principles like context crafting, embedding search, and chaining will remain valid.
Skills adapt to GPT-5, Claude 3, Gemini 2
Agent design is model-agnostic
Digital branding = long-term relevance
Automation will outpace manual coding
“Yes — with your prompt engineering + agent stack, custom GPTs are within reach.”
Answer:
Advanced modules include creating custom GPTs, fine-tuned models, and FastAPI-powered AI services.
Build GPTs for specific business tasks
Deploy with API + UI using FastAPI or Streamlit
Use LangChain to combine GPT + tools
Serve as SaaS or internal AI tools
“You’ll never start from scratch.”
Answer:
Yes, the course includes downloadable templates:
Prompt libraries
Web templates
Branding kits
Resume and portfolio builders
Copy-paste prompt templates
Website layout packs
Infographic generators
Ready-to-edit automation workflows
