6-Month Program · Generative AI
AI Engineering
30 hands-on modules · 18 tools & platforms · 150+ hours of training · Applied generative AI — prompt engineering, API integration and building AI-powered features for real products. Covers LLM APIs, prompt engineering, RAG basics and automation, built around real product-style builds from day one.
Last updated: 27 August 2026
Quick answer
Skill Training Institute's AI Engineering course is a 6-month, 30-module, 150+ hour classroom and online program in Alpha-1, Greater Noida that teaches applied generative AI hands-on: prompt engineering, LLM API integration, retrieval-augmented generation (RAG) and AI-powered product automation. It is beginner-friendly (no prior AI experience required, basic programming familiarity helps), includes 4 portfolio projects and weekly practical assessments, and ends with 100% Placement Assist for roles such as AI engineer, prompt engineer and AI product developer.
Who this course is for
Built for developers, students and career-switchers who want dedicated, focused training on applied generative AI for prompting, LLM APIs, retrieval and automation — no prior AI experience needed, every core concept is taught from the ground up.
By the end you'll write reliable prompts, integrate LLM APIs into real applications, build a working RAG pipeline, and ship an AI-powered product feature the way an applied AI/product engineer would.
1Program structure
Three phases over 24 weeks — from generative AI and prompt engineering fundamentals, through LLM API integration and RAG, to AI-powered product features and automation.
| Phase | Modules | Focus |
|---|---|---|
| Phase 1 — Generative AI & Prompt Engineering Foundations | 01–10 | How LLMs work, prompt engineering techniques, structured outputs and evaluation |
| Phase 2 — LLM API Integration & RAG Systems | 11–20 | Building with LLM APIs, embeddings, vector search and retrieval-augmented generation |
| Phase 3 — AI-Powered Product Features & Automation | 21–30 | Chatbots, workflow automation, tool-calling agents and production deployment basics |
2Full curriculum — all 30 modules
Click any phase to expand its modules, or open a module directly for a detailed description.
Phase 1Generative AI & Prompt Engineering FoundationsModules 01–10How LLMs work, prompt engineering techniques, structured outputs and evaluation+
01Introduction to Generative AI & LLMs+
Understanding what generative AI and large language models are, and where they fit in real products.
02How Large Language Models Work (High-Level)+
Building intuition for how LLMs generate text, without needing the underlying math.
03Prompt Engineering Fundamentals: Structure & Clarity+
Writing clear, well-structured prompts that consistently produce the output you want.
04Prompt Engineering Techniques: Few-Shot, Chain-of-Thought & Role Prompts+
Applying proven prompting techniques to improve accuracy on harder tasks.
05Working with System Prompts & Instructions+
Using system-level instructions to control tone, behaviour and boundaries in an AI feature.
06Structured Outputs: JSON Mode & Function Calling Basics+
Getting reliable, structured data back from an LLM for use in real application code.
07Comparing LLM Providers: OpenAI, Anthropic & Open-Source Models+
Choosing the right model and provider for a given product feature and budget.
08Tokens, Context Windows & Cost Basics+
Understanding tokens and context limits, and estimating the running cost of an AI feature.
09Prompt Testing, Iteration & Evaluation+
Testing prompts systematically and iterating based on real output quality, not guesswork.
10Responsible AI Use: Safety, Bias & Limitations+
Recognising LLM limitations, hallucinations and bias, and designing around them responsibly.
Phase 2LLM API Integration & RAG SystemsModules 11–20Building with LLM APIs, embeddings, vector search and retrieval-augmented generation+
11Setting Up Your First LLM API Project+
Setting up API keys, environments and your first working call to an LLM API.
12Building a Simple Chat Application with an LLM API+
Wiring an LLM API into a basic front-end to build a working chat interface.
13Handling Streaming Responses & Conversation History+
Streaming responses smoothly and managing multi-turn conversation history correctly.
14Introduction to Embeddings & Semantic Similarity+
Turning text into embeddings and using similarity to find related content.
15Vector Databases: Storing & Searching Embeddings+
Storing embeddings in a vector database and querying them for relevant results.
16Building a Retrieval-Augmented Generation (RAG) Pipeline+
Connecting retrieval and generation so answers are grounded in your own documents.
17Chunking Strategies & Document Preparation for RAG+
Splitting and preparing documents so retrieval returns the most useful context.
18Improving RAG Accuracy: Re-ranking & Filtering+
Improving RAG answer quality with re-ranking, filtering and metadata strategies.
19Error Handling, Rate Limits & Reliability in API Apps+
Building AI features that fail gracefully and handle real-world API limits.
20Securing API Keys & User Data in AI Applications+
Protecting API keys and user data across a full AI-powered application.
Phase 3AI-Powered Product Features & AutomationModules 21–30Chatbots, workflow automation, tool-calling agents and production deployment basics+
21Adding AI Features to Existing Web Applications+
Integrating an AI feature cleanly into an existing product's front end and back end.
22Building an AI Chatbot for Customer Support+
Designing and building an AI chatbot that answers support questions accurately.
23Document Summarization & Content Generation Tools+
Building tools that summarise documents and generate content on demand.
24Automating Workflows with LLMs: From Manual Task to Script+
Turning a repetitive manual task into an automated LLM-powered script.
25Introduction to AI Agents & Tool-Calling Basics+
Understanding how an LLM can call tools and functions to take real actions.
26Connecting LLMs to External Tools & APIs+
Wiring an LLM to external APIs so it can fetch data or trigger actions.
27No-Code/Low-Code AI Automation Platforms+
Building AI-powered automations quickly using no-code and low-code platforms.
28Monitoring & Improving AI Feature Performance+
Tracking how an AI feature performs in real use and improving it based on feedback.
29Deploying an AI Feature to Production (Basics)+
Deploying an AI-powered feature so real users can access it reliably.
30Capstone: Building a Complete AI-Powered Product Feature+
Designing, building and deploying a complete AI-powered feature end-to-end.
318 tools & platforms you'll use
Everything is taught using real LLM APIs and platforms you'll use later on the job — not simulations. Swipe or use the arrows to browse every category.
4How each module is taught
Every module follows the same practical, build-it-yourself lens, so you always know what to expect in class.
Concept Walkthrough
Trainer explains the concept with a real product scenario first, then the underlying technique or API.
Live Along
You build the same feature, right alongside the trainer, in your own project.
Hands-on Exercise
An independent task applying the concept to a new prompt, dataset or automation.
Doubt Clearing
One-on-one help for anyone stuck, before the class moves to the next module.
Weekly Assessment
A short practical build or prompt-quality test every week so gaps are caught early, not at the end.
5Projects & portfolio
Four practical, portfolio-ready AI feature projects across the full syllabus — the kind of work you'll show in interviews.
- Project 1. Build an AI chatbot using an LLM API with conversation memory
- Project 2. Build a RAG-based document Q&A tool over your own files
- Project 3. Automate a repetitive business workflow using an LLM
- Project 4. Capstone: a complete AI-powered feature integrated into a product and deployed
6Assessment & certification
Weekly practical assessments covering prompting, API integration, RAG and automation workflows, with instructor review and feedback.
Final practical exam across all three phases, plus the capstone project used as your demonstrable, portfolio-ready proof of skill.
Certificate of completion, plus resume and portfolio support through our 100% Placement Assist program.
7Key terms explained
Short, plain-language definitions of the core terms used throughout this course.
- LLM (Large Language Model)
- An AI model trained on large amounts of text that can understand and generate human-like language, accessed via an API in most real applications.
- Prompt Engineering
- The practice of designing clear, structured instructions for a language model to reliably produce the desired output.
- RAG (Retrieval-Augmented Generation)
- A technique that grounds an LLM's answers in retrieved information from external documents or a database, reducing hallucination and improving accuracy.
- Embeddings
- Numeric vector representations of text that capture meaning, allowing semantically similar content to be found and compared.
- Vector Database
- A database optimised for storing and searching embeddings by similarity, commonly used to power retrieval in RAG systems.
- Tool-Calling / Function Calling
- A capability that lets an LLM invoke external functions or APIs to fetch data or take actions as part of generating a response.
8Quick answers
Do I need any prior AI or coding experience?
Basic familiarity with any programming language helps, but every concept, from prompting to LLM APIs and RAG, is taught from the ground up.
Will I need to pay for LLM API usage during the course?
Most labs are designed to stay within free tiers or minimal cost, and Module 8 covers understanding tokens and costs so you never get a surprise bill.
Do I need my own laptop for the labs?
Any laptop with a browser and internet connection works, since everything is built through the API console and code editor; institute lab computers are also available in class.
Will I build real AI features, or is it mostly theory?
You'll integrate real LLM APIs from week one. Every module includes hands-on practice, ending in a full AI-powered product feature capstone.
Are classes online, offline, or both?
Both. Offline batches run at our Alpha-1, Greater Noida campus; online live batches follow the same curriculum with the same trainers. Morning, evening and weekend timings are available.
What can I do after completing this course?
You'll be ready for AI engineer, prompt engineer and AI product developer roles at startups and agencies, with a client-ready portfolio, and can progress into advanced fine-tuning, agents or MLOps tracks with the same placement support.
Ready to see a class live?
Book a free, no-obligation demo class and sit in on a real AI feature build session before you decide anything.