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Gen AI Engineering: Foundation
Starts from 18th Nov
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LiveStarts from 18th Nov

Gen AI Engineering: Foundation

Part 1 — Foundation Track · 40-Hour Program · Starts from 18th Nov

A practical 40-hour foundation course for developers and technical learners to understand modern LLMs, master prompt engineering, run open-source models, integrate Spring AI, and build production-style Generative AI applications with vector search and citations.

40-Hour ProgramInstructor-led + Hands-on LabsJava & Spring AIEmbeddings & Vector DBEnterprise RAG AssistantStarts from 18th Nov
Live cohort starts from 18th Nov. Limited seats available.
Fee (INR):
₹9,910₹19,820
50% OFF · Limited Time
Overview

Understand Modern LLMs. Build Production-Style GenAI Applications.

Move beyond casual AI prompting and superficial demos. This 40-hour instructor-led foundation program takes software engineers and technical learners deep into how Large Language Models, transformers, tokens, and inference mechanics actually work. You will learn to engineer resilient AI APIs with Java and Spring AI, run local open-weight models via Ollama and Hugging Face, index data into vector databases, and architect an end-to-end grounded Enterprise RAG (Retrieval-Augmented Generation) assistant with source citations and basic evaluation.

Core Technology Scope
GenAI Fundamentals & ArchitectureLarge Language Models & TransformersProduction Prompt Engineering & JSON SchemaModel APIs, Streaming & ResilienceJava & Spring AI IntegrationOpen-Source LLMs & OllamaEmbeddings & Vector DatabasesRAG Pipelines & Enterprise Capstone
Key Advantages

Engineer Real GenAI Systems, Not Just Surface Demos

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Deep Conceptual Mechanics

Understand transformers, attention mechanisms, token counting, context windows, lost-in-the-middle, temperature, Top-P, and inference trade-offs.

✓

Production-Grade Prompt Engineering

Move beyond casual prompting to zero-shot, few-shot, persona design, prompt decomposition, and strict JSON Schema structured output validation.

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Java & Spring AI Core Focus

Learn how to build resilient GenAI microservices with Spring Boot, Spring AI ChatClient, advisors, memory, streaming responses, and circuit breakers.

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Open-Source & Local LLM Mastery

Run local models with Ollama, explore Hugging Face, evaluate quantization (GGUF, INT4/8), and navigate local vs cloud cost/latency trade-offs.

✓

Retrieval-Augmented Generation (RAG)

Ingest PDFs and docs, apply recursive chunking, generate embeddings, index in vector databases (HNSW, pgvector), and deliver grounded answers with citations.

✓

Hands-On Labs & Enterprise Capstone

Every single hour features hands-on practical exercises culminating in a standalone enterprise RAG assistant with architecture defense.

Target Audience

Who Is This Program For?

  • Backend & Full-Stack Developers wanting to integrate modern LLMs and Spring AI into enterprise systems.
  • Java Developers looking to leverage their existing Spring Boot skills for Generative AI engineering.
  • Software Engineers transitioning from traditional application development to AI-powered architectures.
  • Technical Learners & Students seeking a solid, engineering-grade foundation in GenAI, RAG, and vector search.
  • Architects & Tech Leads needing to evaluate model selection, token economics, latency, and private/local vs cloud deployments.
Prerequisites

Starting Requirements

Basic programming experience in Java (or any modern language) and familiarity with REST APIs. No prior machine learning or data science background is required; the course begins from core foundations.

Level: Beginner → Intermediate
Curriculum Breakdown

What You'll Learn

Structured modules designed to build competencies systematically.

•Hour 1 — AI, ML, Deep Learning & Generative AI: AI vs ML vs Deep Learning, Predictive AI vs GenAI, traditional software vs AI-enabled applications, where GenAI fits in enterprise systems [Hands-on: Map 5 business problems to AI/ML/GenAI approaches]
•Hour 2 — Generative AI Capabilities: Text, code, image, audio & video generation, multimodal AI and AI copilots, knowledge assistants & content generation, enterprise use cases & limitations [Hands-on: Identify GenAI opportunities for a sample business]
•Hour 3 — Foundation Models & Model Types: Foundation models & LLMs, reasoning, embedding & vision models, proprietary vs open-weight models, general-purpose vs domain-specific models [Hands-on: Create a structured model-category comparison matrix]
•Hour 4 — GenAI System Architecture: User, application, prompt, model & response flow; where RAG, tools, vector databases, and memory fit; guardrails, APIs, and evaluation components [Hands-on: Draw a reference GenAI enterprise application architecture]
Outcome: Foundational conceptual understanding of Generative AI capabilities, model categories, and reference enterprise system architectures.
Applied Methodology

Learn by Building

Generative AI is best understood by building with it. The program will emphasize practical implementation and real-world application rather than purely theoretical learning.

AI Foundations
LLM & Transformers
Spring AI & APIs
Open Models & Ollama
Embeddings & Vector DB
RAG & Citations
Enterprise Capstone
Real-World Applications

Projects & Practical Deliverables

Build and work with practical applications directly derived from our curricula.

Java · REST APIs · Spring Boot · OpenAI / Claude SDK · SSE Streaming

Project 1: LLM API Integration

Call an LLM securely, configure parameters (temperature, top-p, max tokens), parse structured responses, and implement streaming with Server-Sent Events.

Focus: Model selection, API authentication, streaming responses, error recovery
Spring Boot 3 · Spring AI · ChatClient · Advisors · In-Memory / Redis Memory

Project 2: Java + Spring AI Chat Assistant

Build a production-grade REST-based AI chat assistant featuring Spring AI ChatClient, conversational memory advisors, streaming, and structured DTO output.

Focus: Enterprise microservice architecture, prompt templates, structured output
Ollama · Llama 3 / Mistral · GGUF Quantization · Spring AI OllamaChatModel

Project 3: Local LLM Application

Run local open-weight models (Llama 3, Mistral) on your machine with Ollama and connect them to a Java application for private, offline AI execution.

Focus: Open-source models, local inference, quantization trade-offs, zero-cloud privacy
Embedding Models · Vector DB (pgvector / Chroma) · HNSW Indexing · Spring AI

Project 4: Semantic Search Engine

Embed enterprise text into high-dimensional vector representations, store them in a vector database, and perform semantic similarity searches using HNSW indexing.

Focus: Vector representations, cosine similarity, ANN indexing, metadata filtering
Document Loaders · Recursive Chunking · Vector Store · Spring AI · Source Citations

Project 5: Enterprise Document Q&A

Build a production RAG system with document ingestion, recursive chunking, vector retrieval, grounded prompt assembly, and verifiable source citations.

Focus: Document parsing, chunking strategies, grounded answering, citation verification
Spring Boot · Spring AI · pgvector / Chroma · Grounded RAG · Citations · Evaluation

Project 6: Basic Capstone — Enterprise RAG Assistant

Deliver a complete standalone enterprise RAG assistant with multi-document ingestion, semantic search, grounded responses with inline citations, and retrieval evaluation.

Focus: End-to-end enterprise AI engineering, architectural defense, interview readiness
End-to-End Capstone

Basic Capstone: Enterprise RAG Assistant with Citations & Evaluation

A complete, production-grade knowledge assistant that ingests private enterprise documentation, indexes semantic vectors in a database, retrieves relevant context, and generates grounded answers accompanied by verifiable source citations and strict hallucination guardrails.

Integrated Architecture Workflow

1.Requirements & Architecture: Define business problem, users, API contracts, model selection (cloud and local), and vector database selection
2.Ingestion & Chunking: Ingest PDF, Word, and text documents using recursive splitting and metadata extraction
3.Embeddings & Vector Storage: Generate vector embeddings and persist in vector index (HNSW / pgvector) with metadata tags
4.Retrieval & Augmentation: Execute top-K similarity search with metadata filtering and assemble grounded prompts
5.Grounded Generation & Citations: Generate answers with explicit document citations and strict 'no hallucination' fallback behavior
6.Evaluation & Interview Presentation: Benchmark retrieval accuracy and faithfulness against a test question set, present live demo, and defend architecture
Program Specifications

Instructor-Led Live Cohort + Hands-On Labs

DetailInformation
ProgramGen AI Engineering: Foundation
TrackPart 1 of 2 (Foundation Track)
FormatInstructor-led + Hands-on Labs
Duration40 Hours (9 Modules, 40 Hourly Sessions)
LevelBeginner → Intermediate
Core StackLLMs, Prompt Engineering, Java, Spring AI, Ollama, Vector DB, RAG
Next StepPart 2 — Advanced Generative AI Engineering (40 Hours)
Cohort StartStarts from 18th Nov
Interactive Cohort Model

Learn With an Active Engineering Cohort

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Live Instructor-Led Sessions

Interactive architectural lectures and code walkthroughs for all 40 hours.

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Hands-on Labs in Every Hour

Reinforce every conceptual hour with practical coding, testing, and system experiments.

✓

Live Code Reviews & Debugging

Solve real runtime issues, token latency problems, and API edge cases together.

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Uncorp Community

Collaborate with peers, mentors, and industry engineers in our private WhatsApp channel.

Package Breakdown

Everything You Need for the Journey

40 Hours of live instructor-led sessions
Hands-on labs in every single hour
6 practical projects including enterprise capstone
Uncorp WhatsApp Community & mentor access
Instructor Experience

Learn From Industry Experience

Learn from an experienced technology professional with 12+ years of industry experience.

Community Support

Learn With the Uncorp Community

Join the Uncorp WhatsApp Community and connect directly with fellow AI builders, alumni, and instructors.

*Uncorp does not promise 24/7 support, guaranteed response time, personal mentoring, or 1-on-1 private doubt solving unless formally announced.

Uncorp WhatsApp CommunityLearner & Team Network
Status Overview

Program Information

DetailStatus
ProgramGen AI Engineering: Foundation
TrackPart 1 of 2 (Foundation Track)
FormatInstructor-led + Hands-on Labs
Duration40 Hours
Curriculum9 Modules, 40 Hourly Sessions
Projects6 Hands-on Deliverables + Capstone
ScheduleStarts from 18th Nov
Price
₹9,910₹19,82050% OFF
Next TrackPart 2: Advanced Gen AI Engineering (40 Hours)
Cohort Enrollment

Program Pricing & Enrollment

Inaugural live cohort starts 18th Nov. Early registrations receive access to prep material and community onboarding.

50% LIMITED TIME OFFER
Program Fee (INR)
₹9,910₹19,820
You Save ₹9,910 (50% OFF)
Special cohort pricing · 100% money-back satisfaction guarantee
Includes:
  • Inaugural live cohort starting 18th Nov
  • All live session recordings for review
  • 6 project starter kits, vector store configs & lab exercises
  • Uncorp WhatsApp Community membership
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