LLM Development Overview

Master Prompt Engineering, Fine-tuning Models, LangChain, Vector Databases, and AI Application Deployment — All in One Program!

Rohil NextGen offers a complete LLM Development training covering Prompt Engineering, Fine-tuning, LangChain Integrations, RAG Architecture, and AI App Deployment with real-world projects using OpenAI, Hugging Face, Pinecone, and AWS.

TOP RATED 4.9 4.9 Ratings
6 Months
Course Duration
2025
Latest Curriculum
180
Total Hours
100
Practical Hours

Why Choose LLM Development Certification?

Annual Income

Estimated Salary

₹ 28 L

Career Opportunities

Growth Rate This Year

75%

Industry Demand

Projected by 2026

7 L

Become an LLM Developer

❝ Learn Prompt Engineering, Fine-tuning, and AI App Development ❞

Course Overview

This LLM Development course is designed to teach you to build intelligent AI-powered applications. Learn Prompt Engineering, Fine-tuning open-source models, work with LangChain frameworks, build Retrieval-Augmented Generation (RAG) apps, and deploy AI services on the Cloud.

Key Learning Outcomes

  • Master Prompt Engineering and LLM model customization.
  • Fine-tune and deploy open-source LLMs using Hugging Face.
  • Integrate Vector Databases (Pinecone, FAISS) for RAG architecture.
  • Build intelligent apps using LangChain and OpenAI APIs.
  • Deploy AI Applications on AWS, Azure, or GCP.

Career Prospects with This Course

  • Become an LLM Engineer or AI Developer for global companies.
  • Build smart AI-powered Chatbots and Automation Systems.
  • Manage AI pipelines and Vector Databases for efficient retrievals.
  • Work closely with Data Scientists and DevOps Engineers for AI projects.
  • Start your own AI consulting business or freelance as an LLM Specialist.

LLM Development & Applications – 180 Days Syllabus

Phase 1: NLP & Deep Learning Foundations (Days 1–30)

Theory

  • Introduction to NLP and Deep Learning
  • Neural Networks and Backpropagation
  • Word Embeddings and Text Representations
  • Sequence Models and RNNs

Practical

  • Implement basic neural networks
  • Create word embeddings from scratch
  • Build simple RNN models for text classification

Phase 2: Transformers & Attention Mechanism (Days 31–60)

Theory

  • Attention Mechanism Fundamentals
  • Transformer Architecture Deep Dive
  • Self-Attention and Multi-Head Attention
  • Positional Encoding and Layer Normalization

Practical

  • Implement attention mechanism from scratch
  • Build transformer encoder and decoder blocks
  • Create a simple transformer model

Phase 3: Hugging Face Transformers (Days 61–90)

Theory

  • Hugging Face Ecosystem Overview
  • Pre-trained Models and Tokenizers
  • Model Hub and Pipeline API
  • Custom Model Training with Transformers

Practical

  • Use pre-trained models for various NLP tasks
  • Implement custom tokenizers
  • Fine-tune models on custom datasets

Phase 4: Fine-Tuning & Custom Training (Days 91–120)

Theory

  • Transfer Learning for NLP
  • Parameter-Efficient Fine-tuning (PEFT)
  • LoRA and QLoRA Techniques
  • Model Evaluation and Metrics

Practical

  • Fine-tune models on domain-specific data
  • Implement LoRA for efficient training
  • Evaluate model performance comprehensively

Phase 5: LLM APIs & Low-Code Interfaces (Days 121–140)

Theory

  • OpenAI API and GPT Models
  • Prompt Engineering Best Practices
  • Function Calling and Tool Usage
  • Cost Optimization and Rate Limiting

Practical

  • Build applications using OpenAI API
  • Implement advanced prompt engineering
  • Create function calling applications

Phase 6: LLM Deployment & Integration (Days 141–160)

Theory

  • Model Deployment Strategies
  • Containerization with Docker
  • Cloud Deployment (AWS, Azure, GCP)
  • API Development and Management

Practical

  • Deploy models using FastAPI
  • Containerize applications with Docker
  • Deploy to cloud platforms

Phase 7: Ethics, Safety, & Capstone (Days 161–180)

Theory

  • AI Bias, Fairness, Explainability
  • Red teaming and prompt filtering
  • RLHF (Reinforcement Learning with Human Feedback)
  • Open Source vs Proprietary LLMs

Practical

  • Implement bias detection and mitigation
  • Create red teaming pipelines
  • Build and deploy capstone projects

Capstone Project Ideas

  • Legal Assistant Bot (LLM + RAG)
  • Automated Report Generator
  • HR Resume Screener with GPT
  • Voice-to-Text Summarizer App

Deliverables

  • GitHub project with README
  • Deployed API/web app
  • Documentation or walkthrough video

Frequently Asked Questions

What is LLM Development?

LLM Development involves creating, fine-tuning, and deploying Large Language Models for various applications. It includes prompt engineering, model customization, building AI applications using frameworks like LangChain, and deploying them to production environments.

Do I need prior AI/ML experience for this course?

Basic programming knowledge (preferably Python) is recommended, but no prior AI/ML experience is required. The course starts with fundamentals and gradually builds up to advanced LLM development concepts, making it suitable for beginners and professionals looking to specialize in AI.

Will I receive a certificate after completing the LLM Development course?

Yes, upon successful completion of the course and capstone project, you will receive a certificate from Rohil NextGen that validates your LLM Development skills and can be shared with employers.

What kind of projects will I work on during the course?

You'll work on real-world LLM projects including chatbot development, fine-tuning models for specific domains, building RAG applications, creating AI-powered automation systems, and deploying production-ready AI services. The capstone project allows you to build a comprehensive application integrating all learned concepts.

What tools and platforms will I learn?

You'll learn industry-standard tools including Hugging Face Transformers, OpenAI API, LangChain, Vector Databases (Pinecone, FAISS), cloud platforms (AWS, Azure, GCP), Docker for containerization, and various MLOps tools for model deployment and monitoring.

What career support do you provide after course completion?

We provide comprehensive career support including resume building, interview preparation, portfolio development guidance, and connections with our placement partners for job opportunities as LLM Engineers, AI Developers, and AI specialists in tech companies and startups.