LLM Fundamentals
Understand language models, their capabilities, context, and limitations.
LLMS & RETRIEVAL-AUGMENTED GENERATIONCURRICULUM PREVIEW
LLMs & Retrieval-Augmented Generation
Course fee: INR 25,000 + 18% GST (INR 4,500). Total including GST: INR 29,500.
Confirm exclusions, materials, assessments and payment options with admissions.
Build Intelligent AI Applications with LLMs & RAG
Learn how to transform language models into useful AI applications that retrieve relevant information, understand context, and generate grounded responses.
Learn. Build. Demonstrate.
60 hourscourse duration
Contact admissionsupcoming batch
Confirm online / offline availabilitylearning mode
Course duration: 60 hours. Contact admissions for batch dates, daily timings and delivery arrangements.
Program Highlights
RAG Development
Connect AI responses with relevant documents and knowledge sources.
Hands-on Coding
Build and test practical AI application workflows.
Career Readiness
Develop demonstrable project skills.
Program Overview
Understand language models. Build grounded AI applications.
Learn how Large Language Models (LLMs) work and how to build AI applications that retrieve relevant information, understand context, and generate useful responses. This program introduces practical development of AI chatbots, document question-answering systems, knowledge assistants, and intelligent business applications using Retrieval-Augmented Generation (RAG).
At E Careerpluz, progress from understanding LLM fundamentals to developing AI solutions using Python, embeddings, vector databases, retrieval pipelines, and modern AI frameworks.
What you will learn
- Understand Large Language Models and Generative AI fundamentals.
- Build AI-powered chatbots and conversational applications.
- Develop RAG pipelines using private documents and knowledge sources.
- Work with embeddings, semantic search, and vector databases.
- Connect LLMs to business data, APIs, and document collections.
- Evaluate AI responses for relevance, accuracy, and groundedness.
- Create portfolio-ready projects for practical applications.
Who should enroll?
Python developers
Extend existing Python skills into language-model application development.
Software engineers
Explore LLM APIs, retrieval pipelines, and AI application architecture.
Data science learners
Apply data preparation and analysis skills to document retrieval and evaluation.
AI enthusiasts and IT professionals
Build a foundation in LLMs, embeddings, and responsible AI application design.
Graduates
Develop practical skills through a guided curriculum and portfolio projects.
Eligibility
- Basic Python programming is recommended.
- Familiarity with APIs and introductory machine-learning concepts is helpful.
- Beginners can start with a Python foundation module; confirm suitability with admissions.
Curriculum Preview
A modular learning pathway progresses from core concepts to retrieval pipelines, application development, evaluation, and deployment.
Module 1: Introduction to Generative AI and LLMs
- Generative AI concepts and applications.
- How Large Language Models work.
- Tokens, context windows, prompts, and model limitations.
- Overview of open-source and hosted LLMs.
Module 2: Python and LLM API Integration
- Python essentials for AI development.
- REST APIs, JSON, and environment variables.
- Connecting to language model APIs.
- Handling errors, outputs, and token usage.
Module 3: Prompt Engineering and Structured Outputs
- System and user prompts.
- Few-shot prompting and instruction design.
- Structured JSON responses.
- Output validation and prompt evaluation.
Module 4: Embeddings and Semantic Search
- Text embeddings and similarity measures.
- Document loading, cleaning, and chunking.
- Metadata and indexing strategies.
- Semantic search and retrieval techniques.
Module 5: Retrieval-Augmented Generation (RAG)
- RAG architecture and workflow.
- Vector databases and document retrieval.
- Context construction and grounded generation.
- Retrieval filtering, reranking, and hybrid search.
- Citations and source-aware answers.
Module 6: LLM Frameworks and Application Development
- LangChain and LlamaIndex fundamentals.
- Conversational memory and chat history.
- Tool calling and external API integration.
- Building document assistants and chat interfaces.
Module 7: Evaluation, Security, and Deployment
- Testing answer relevance and retrieval quality.
- Hallucination reduction and groundedness checks.
- Prompt injection and sensitive-data protection.
- Application deployment, monitoring, and optimization.
Module 8: Capstone Project
- Design an end-to-end RAG application.
- Test using a representative dataset.
- Present project architecture and results.
- Prepare a portfolio and project demonstration.
Final module coverage, depth, delivery format, and course duration depend on the selected batch. Confirm the approved syllabus with ECAREERPLUZ before enrolling.
Tools & Technologies
Learn the technologies behind modern AI applications
Explore tools used to develop, connect, retrieve, evaluate, and deploy LLM-powered applications. Exact technology coverage depends on the final syllabus.
Programming and model APIs
Python
Build LLM applications, process documents, integrate APIs, and create RAG pipelines.
Applications: scripting, data processing, and API integration.
OpenAI API
Integrate supported language models into chatbots, document assistants, structured-output workflows, and AI applications.
Applications: text generation, embeddings, and tool calling.
LLM frameworks
LangChain
Build LLM workflows, connect models to retrieval systems, and integrate external tools and data sources.
LlamaIndex
Explore document ingestion, indexing, retrieval, and knowledge-grounded AI application patterns.
Vector search and model resources
Chroma
Store and retrieve vector embeddings for semantic search and document-based question answering.
FAISS
Explore efficient similarity search over vector embeddings for retrieval applications.
Hugging Face
Discover pretrained language models, tokenizers, embedding models, and open-source AI resources.
Application development
Streamlit
Turn Python code into interactive AI chat interfaces and document-query applications.
FastAPI
Expose AI and RAG functionality through APIs that can connect with websites, dashboards, and software.
Data and deployment
PostgreSQL with pgvector
Explore relational data with vector similarity search.
Docker and Git
Learn application packaging, version control, and collaboration fundamentals.
Development environment
Jupyter Notebook / Google Colab
Experiment with models, explore data, and practise coding in interactive notebooks.
Discuss tool access and any separate provider accounts with admissions. Explore technologies used in LLM and RAG development. Confirm which tools and training environments are available for your batch. Confirm software access, subscriptions, model-provider terms, usage limits, and any additional charges with admissions. No certification or vendor partnership is implied; product names and trademarks belong to their respective owners.
Projects & Practical Work
Turn AI concepts into working applications
Build projects that demonstrate how LLMs can retrieve information, answer questions, and assist users with practical tasks.
PROJECT 1
AI Document Chatbot
Upload PDFs, notes, manuals, or policy documents and ask questions in natural language.
Skills: Document processing, embeddings, retrieval, answer generation.
PROJECT 2
Educational Knowledge Assistant
Create a learning assistant that answers student questions using approved course materials and provides source references.
Skills: RAG pipelines, source citations, conversational interfaces.
PROJECT 3
Customer Support AI Assistant
Develop a support assistant that retrieves answers from FAQs, product documentation, and service guidelines.
Skills: Knowledge retrieval, prompt design, API integration.
PROJECT 4
Business Knowledge Search Engine
Build a searchable knowledge assistant for company documents, internal procedures, and operational information.
Skills: Metadata filtering, semantic search, access-control concepts.
PROJECT 5
End-to-End RAG Application
Combine document ingestion, retrieval, a language model, an interactive interface, evaluation, and deployment into one complete project.
Skills: System design, testing, optimization, deployment.
Build and evaluate your RAG application
- Define an application goal and identify suitable, authorized source documents.
- Prepare documents, create chunks and embeddings, and build a retrieval workflow.
- Connect the retrieved context to a language model and user interface.
- Evaluate retrieval quality, answer relevance, source grounding, and limitations.
- Document architecture, test the application, and demonstrate the project.
Project outcome: build a practical portfolio that demonstrates the ability to design, implement, test, and explain LLM and RAG solutions.
Learning Goals
What will you be able to do?
By the end of the program, learners should be able to:
Explain LLMs, tokens, embeddings, context windows, and generative AI workflows.
Develop Python applications that communicate with language model APIs.
Design effective prompts and structured-output workflows.
Process and index documents for semantic retrieval.
Build RAG pipelines that ground answers in relevant source material.
Work with vector databases and retrieval frameworks.
Improve retrieval quality through chunking, filtering, and reranking.
Evaluate answer quality, relevance, and factual grounding.
Apply basic security practices to protect data and reduce prompt-injection risks.
Build and demonstrate an end-to-end AI application.
Learn through guided instruction, practical coding, structured assignments, and project demonstrations that connect concepts with application development.
Industry Salary Growth & Business Opportunities
Build skills for the growing AI application ecosystem
LLMs and RAG are used across industries for intelligent search, document analysis, customer support automation, and knowledge management. This program can help learners build relevant skills for entry-level AI application development; specialized engineering roles may require additional experience.
Career pathways
LLM Application Developer
Develop AI-powered applications, integrate model APIs, and build conversational interfaces.
RAG Engineer / AI Engineer
Develop retrieval pipelines, knowledge assistants, semantic search systems, and LLM integrations.
Python AI Developer
Build backend services, integrate AI APIs, and connect AI systems to business applications.
AI Solutions Consultant
Identify business use cases and help organizations adopt practical AI solutions.
Indicative salary progression in India
Compare these planning ranges with current local opportunities. Pay varies by role, qualifications, experience and employer.
Junior Python / AI Application Developer
INR 3-6 LPAIndicative annual salaryGenerative AI Application Developer
INR 5-12 LPAIndicative annual salaryRAG / AI Engineer
INR 6-15 LPAIndicative annual salaryExperienced AI Engineer / Specialist
INR 12-25+ LPAIndicative annual salaryBusiness opportunities
- Build custom AI chatbots for businesses.
- Develop document-based assistants for schools and training institutes.
- Create customer-support automation solutions.
- Offer internal knowledge-search systems to companies.
- Integrate AI assistants into existing websites and software.
- Develop AI prototypes for startups and small businesses.
- Provide LLM application consulting and workflow integration services.
Build your future: Learn to solve business problems with AI, not just use AI tools.
Placement Assistance
From practical learning to career preparation
E CAREERPLUZ supports learners in preparing for opportunities in AI application development through career guidance and project-based learning.
Career support services
Resume & Portfolio
Develop a resume that highlights Python, LLM, and RAG skills and organize projects for a GitHub portfolio.
Technical Interview Practice
Practise Python programming, AI fundamentals, and project-based technical questions.
Project Presentation
Learn to explain project architecture, retrieval choices, evaluation, and design decisions.
Career Guidance
Explore relevant roles, job-search approaches, and employer requirements.
Interview Preparation
Practise communicating technical concepts and discussing practical AI projects.
Actual opportunities depend on learner performance, eligibility, and employer requirements. Confirm current support services with E CAREERPLUZ.
Example learning journeys
From learning AI to building intelligent solutions
Explore example LLMs and RAG application development projects and learning journeys.
Example project showcase01
AI Document Assistant Project
Example project showcase
A learner builds a document question-answering application that retrieves information from uploaded study materials and generates source-grounded responses.
Practice focus: Python, embeddings, RAG, and source citations.
Example project showcase02
Customer Support AI Solution
Example project showcase
A learner creates a support chatbot that searches FAQs and product documents to provide relevant answers to common customer queries.
Practice focus: Prompt engineering, retrieval, APIs, and chatbot development.
Example project showcase03
End-to-End RAG Application
Example project showcase
A learner develops and presents a complete RAG workflow, including document processing, retrieval, answer evaluation, and a user-friendly interface.
Practice focus: Application architecture, testing, and deployment fundamentals.
Data-Driven Learning, Delivered with Quality
A practical, measurable approach to AI education
Our learning approach combines guided instruction, coding exercises, structured assessments, and project reviews to support practical skills in LLM and RAG application development.
Our learning approach
Structured Learning Path
Progress from foundational concepts to retrieval pipelines, application development, and deployment.
Hands-On Practice
Reinforce concepts through guided coding exercises, assignments, and practical problem-solving.
Progress Tracking
Use quizzes, coding tasks, assignment reviews, and project milestones to identify learning gaps.
Responsible AI Practices
Consider data privacy, source grounding, response quality, and safe application design.
Our learning objective is to help learners move from understanding AI concepts to building useful, testable, and maintainable AI applications.
Meet Your Mentors
Learn from Industry Experts and Experienced Professionals.
Mr. P. PRAKASH RAJ
Software Developer
Mrs. S. SIVANISHA
IT Trainer
Mr. S. MOHAMED RIYAS KHAN
UI & UX Designer
Mr. S. SABARINATHAN
AI AnalystFrequently Asked Questions
Everything You Need to Know Before Enrolling
What is an LLM?
A Large Language Model is an AI model trained to understand and generate language. It can support tasks such as summarization, question answering, and conversational applications, but its outputs can be incorrect and should be evaluated.
What is Retrieval-Augmented Generation (RAG)?
RAG combines information retrieval with language-model generation. An application retrieves relevant content from an approved knowledge source and supplies that context to the model to help ground its response.
Why should I learn RAG along with LLMs?
RAG is one approach to connecting a language model with selected documents or knowledge sources. It can improve relevance and traceability. Evaluate retrieval and generated answers for accuracy.
Do I need Python knowledge to join?
Basic Python programming is recommended for the practical application modules. Beginners can ask admissions about a Python foundation module and preparation options.
Do I need a mathematics or machine-learning background?
Introductory machine-learning knowledge is helpful but the required level depends on the approved batch curriculum. Confirm prerequisites with admissions.
What projects will I build?
These practice projects include document question answering, an educational knowledge assistant, customer-support retrieval, a business knowledge search engine, and an end-to-end RAG application.
Which tools and frameworks will I learn?
Suggested tools include Python, language-model APIs, LangChain, LlamaIndex, Chroma, FAISS, Hugging Face, Streamlit, FastAPI, PostgreSQL with pgvector, Docker, Git, and Jupyter or Colab. Final coverage depends on the approved syllabus.
How long does the course take to complete?
The course duration is 60 hours. Contact admissions for batch dates and the detailed timetable.
Will I receive a certificate?
Confirm the certificate issuer, attendance requirements, assessment criteria, and completion terms with E CAREERPLUZ before enrolling. No vendor certification or third-party accreditation is implied.
Does the course include placement assistance?
Ask about available support, including resume and portfolio guidance, interview practice, career guidance, and project presentation. Confirm current services with the institute.
Can I use these skills for freelancing or business?
These skills may support services such as document assistants, chatbots, internal search, and AI integrations. Commercial use depends on client needs, privacy and security requirements, provider terms, and your experience.
How do I enroll in the LLMs & RAG program?
Use the enquiry form or contact E CAREERPLUZ to confirm the final syllabus, prerequisites, duration, fees, learning format, and upcoming batch before deciding.
What is the course fee, including GST?
Course fee: INR 25,000 + 18% GST (INR 4,500). Total including GST: INR 29,500. Confirm exclusions, materials, assessments and payment options with admissions.
What is the course duration?
The course duration is 60 hours. Contact admissions for batch dates and the detailed timetable.
Build your next chapter in LLMs and RAG application development.
Confirm course duration, fees, upcoming batch, learning mode, and final curriculum with our team.
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