GENERATIVE AI APPLICATION DEVELOPMENT
EDUCORA CAREER PROGRAMMastering LangChain: Generative AI, RAG & AI Agents
Learn to develop Generative AI applications using Python, LangChain, RAG and LangGraph, with practical coding, evaluation and a capstone.
3 months training + 3 months internship
GENERATIVE AI APPLICATION ENGINEERING
Build practical Generative AI applications.
Educora’s Mastering LangChain program combines a practical Python foundation with applied Generative AI engineering, LangChain, Retrieval-Augmented Generation, vector databases, AI agents and LangGraph.
The first three months develop your coding skills through technical learning and practical exercises. After training and the required assessments, the following three-month Internship focuses on supervised application development, testing, evaluation and documentation.
Prepare documents and metadata02 · Retrieve
Find useful context using embeddings03 · Orchestrate
Connect models, tools and controlled workflows04 · Evaluate
Review grounded responses and application behavior
A CLEAR LEARNING PATH
More than a list of topics
Develop practical AI application skills through structured technical learning, guided exercises and project work.
Python for AI Applications
Develop Python foundations, reliable code, API handling and data validation.
LangChain & LLM Integration
Connect models, prompts and structured outputs in application workflows.
Retrieval-Augmented Generation
Retrieve useful context and generate source-grounded responses.
Vector Databases & Embeddings
Prepare documents and explore embedding-based retrieval with FAISS and Chroma.
AI Agents & LangGraph
Explore tool calling, stateful workflows, routing and human approval.
AI Evaluation, Security & Deployment
Test application behavior, review reliability and plan controlled deployment.
FIND YOUR STARTING POINT
Who can join this program?
Review the audience and prerequisites to see how the program fits your background.
BCA, MCA, B.Sc. IT and BE/B.Tech students or graduates with suitable coding preparation
Software developers interested in AI-powered applications
Python developers seeking LLM integration skills
Data science and ML professionals expanding into Generative AI
Automation developers and technical professionals
Prerequisites and equipment
- Basic computer proficiency and consistent coding practice are essential.
- Prior Python knowledge is helpful but not mandatory if you complete the recommended preparatory work.
- Complete programming beginners may need additional preparation.
- A personal laptop capable of running Python development tools and reliable internet access are recommended.
- Required model API, cloud and external service costs will be clarified before enrolment.
FROM LEARNING TO APPLICATION
Your learning journey
Python & LLM foundations
Build the foundations.
LangChain & RAG
Practise with guided coding tasks.
LangGraph & capstone
Demonstrate your work.
Internship
Apply your learning through supervised work.
COMPLETE TRAINING SYLLABUS
Your 3-Month Structured Curriculum
Three months of technical training. Progress from Python foundations to retrieval, agents and a capstone application. Explore the complete training sequence below.
Ask About This ProgramWeek 1 — Python Foundations for AI Applications
Python syntax, data structures, functions, modules, file operations, environments and developer tooling.
Week 2 — Python APIs & Reliable Programming
Object-oriented Python, REST APIs, JSON, Pydantic, exception handling, logging, testing and Git.
Week 3 — Generative AI & LLM Fundamentals
Language models, tokens, context windows, API-based integration, prompting, structured outputs and responsible handling of model inputs.
Week 4 — LangChain Application Development
LangChain components, prompts, models, runnables, structured output, tracing and workflow evaluation.
Week 5 — Document Processing & Embeddings
Document loading, cleaning, chunking, metadata, embeddings and ingestion pipeline development.
Week 6 — Vector Databases & RAG
Vector search, retrieval strategies, FAISS/Chroma, grounding, citations and retrieval-augmented applications.
Week 7 — Advanced RAG Techniques
Hybrid retrieval, reranking, query rewriting, context optimization and evaluating answer quality.
Week 8 — Tool Calling & API Integration
Function calling, custom tools, external APIs, permissions, validation, error handling and safe workflow execution.
Week 9 — AI Agents & LangGraph
Agent patterns, graph state, routing, conditional workflows, memory concepts and human approval steps.
Week 10 — Multi-Agent Workflows & MCP
Introductory multi-agent coordination, agent handoffs and Model Context Protocol integration concepts.
Week 11 — Production-Ready AI Applications
Fine-tuning decisions, application security, monitoring, logging, caching, cost control and deployment planning.
Week 12 — Capstone Integration & Career Preparation
Application integration, tests, evaluation, documentation, technical presentation and portfolio preparation.
GUIDED LABS & CAPSTONE THEMES
Projects and portfolio
Proposed learning exercises and capstone themes. Actual assignments may vary by cohort and assessment requirements.
Enterprise Knowledge Assistant
Build a document-aware assistant that retrieves relevant information and generates source-grounded responses.
AI Research Assistant
Create a workflow that collects information, summarizes findings and communicates supporting references and uncertainty.
AI Workflow Automation Assistant
Develop a controlled tool-using assistant that performs approved tasks with validation and human confirmation where required.
LangGraph Multi-Agent Workflow
Explore coordinated analysis and review using bounded multi-agent patterns.
Skills you will practise
- Python programming for AI application development
- REST APIs, JSON, Pydantic and reliable application code
- Large Language Models and structured prompting
- LangChain application workflows
- Document processing and vector embeddings
- Retrieval-Augmented Generation (RAG)
- Vector databases and retrieval optimization
- AI tool calling and API integration
- LangGraph workflows and agent orchestration
- Introductory multi-agent systems and MCP integration
- AI application evaluation, security and observability
- Capstone development, testing and documentation
AFTER COMPLETING TRAINING
An actual 3-month company internship
After completing training and the required assessments, eligible learners undertake a three-month Internship under the confirmed program arrangements.
Month 4 — Discovery & Prototyping
Define an appropriate business problem, prepare solution requirements, design the architecture and build a working prototype.
Month 5 — Integration & Quality Engineering
Implement retrieval or agent workflows, integrate approved tools, test application behavior and evaluate reliability, security and performance.
Month 6 — Deployment & Project Handover
Prepare an approved deployment or demonstration, complete documentation, review results and present the finished application.
Internship documentation reflects the verified activities and dates completed and the issuing organization’s policies. Enquire about host arrangements, work mode, timings and any applicable stipend. Internship participation does not guarantee employment.
EXPLORE YOUR NEXT STEP
Career opportunities
Suitability depends on prior experience, demonstrated skills and employer requirements. These pathways are illustrative; employment, salaries and placement are not guaranteed.
PRESENT YOUR WORK WITH CONFIDENCE
Career preparation
Instructor-led technical learning
Explore concepts and application development with structured instruction.
Guided coding exercises
Practise the technical skills covered in the weekly syllabus.
Module assessments
Review understanding through coding tasks and feedback.
Applied AI development
Connect retrieval, models and tools in controlled applications.
Capstone evaluation
Review application behavior, tests, documentation and presentation.
Supervised practical work
Apply learning through the approved internship work plan.
Attendance requirements, assessment thresholds and certificate eligibility follow Educora’s approved program policy. Confirm the applicable completion conditions before enrolment.
BEFORE YOU ENROL
Frequently asked questions
What is the program duration?
Six months in total: three months of training followed by three months of internship.
Is this program suitable for beginners?
An accelerated Python foundation is included. Complete coding beginners may need additional pre-course study and practice.
Will I learn LangChain and LangGraph?
Yes. Both are central to the technical curriculum, including LangChain workflows and LangGraph-based agent patterns.
Does the curriculum cover RAG?
Yes. You will study document ingestion, embeddings, vector retrieval, cited responses and retrieval evaluation.
Will I learn AI agents and MCP?
Yes. The program covers practical agent workflows and introductory MCP integration.
Does it cover fine-tuning?
Fine-tuning approaches and decision-making are introduced. Any hands-on small-model demonstration depends on available resources.
Are paid AI API subscriptions required?
Provider selection and any additional costs will be explained before enrolment.
What happens after the training phase?
Eligible learners move to the confirmed three-month Internship after completing training and the required assessments.
Will I receive an internship or experience letter?
Documentation depends on the verified activities completed and the applicable issuing organization’s policies.
Is job placement guaranteed?
No. The program develops practical skills and portfolio evidence, but employer hiring decisions are independent.
LET’S DISCUSS YOUR GOALS
Choose your next step with Educora.
Ask about eligibility, the syllabus, fees, upcoming batches and the internship.