BACKEND
Backend Developers
Best for developers who want to build retrieval-backed APIs, model-connected workflows, and practical AI features inside real products.
APIRAG
AI Developer Course
Learn RAG, AI Agents, Modern AI Frameworks, and AI App Development
This AI Developer Course is a hands-on program for backend, frontend, and full-stack developers who want to build modern AI applications. Learn how to work with model APIs, retrieval workflows, AI agents, structured tool use, and practical application patterns used in real software products.
Built for software engineers who want practical exposure to RAG, agent workflows, modern model ecosystems, and developer-first AI implementation.
- Build retrieval-backed AI features for real applications
- Work with AI agents, tool calls, and multi-step workflows
- Explore modern AI frameworks and model ecosystems
- Build stronger AI development skills with practical projects
The learning experience is designed around modern AI development workflows, including exposure to RAG, agentic patterns, model ecosystems such as GPT, Claude, and Gemini, and frameworks commonly discussed by developers such as LangChain, LangGraph, CrewAI, and AutoGen.
Live ILT + Guided ProjectsStructured developer-first syllabusMentor-led implementation support
Limited seats for the next cohort
Book a Free Session
Discuss your goals with our AI engineering team
Why Developers Choose This AI Developer Course
Python, APIs, and AI App Foundations
Build a strong base in Python, API handling, FastAPI, and application-first AI development patterns that help software engineers move into real AI product work.
RAG and Retrieval-Backed Development
Learn how retrieval-backed AI applications are built using document understanding, knowledge workflows, and grounded response patterns for practical use cases.
AI Agents and Multi-Step Workflows
Work with AI agents, tool-connected workflows, and multi-step logic so you can understand how modern AI systems support real tasks beyond simple prompting.
Modern Model APIs and Ecosystem Exposure
Get practical exposure to modern model ecosystems and developer workflows across widely used APIs, helping you understand how AI features are integrated into applications.
Developer Tools, Frameworks, and App Building
Explore the app-building side of AI development through modern frameworks, developer tools, and interface patterns used in practical AI application work.
Mentorship, Projects, and Practical Guidance
Learn with guided implementation support, structured projects, and mentorship that helps developers turn concepts into working AI builds with stronger confidence.
Admissions open
Next live batch: 15th–30th
Limited seats (small batch)
Application Layer (UI and APIs)
- Build practical application flows such as chat interfaces, search experiences, and AI-powered product features.
- Work with API endpoints, sessions, streaming responses, and developer-friendly integration patterns.
Model Layer (LLMs and Model APIs)
- Learn how modern AI applications work with model APIs and structured prompting patterns.
- Understand how developers use models for reasoning, generation, extraction, and workflow support inside software products.
Retrieval Layer (Knowledge and RAG Workflows)
- Work with PDFs, web content, and structured data sources using retrieval and grounding patterns.
- Understand chunking, metadata, filtering, and retrieval design for better RAG-based application behavior.
Agent Layer (Tools, Actions, and Multi-Step Logic)
- Explore tool-connected AI workflows that can trigger actions, call functions, and support multi-step task execution.
- Understand how agents fit into practical software use cases without treating every workflow like a full autonomous system.
Reliability Layer (Structured Outputs, Evaluation, and Safer Responses)
- Use structured outputs, response controls, and practical safeguards to make AI features more reliable in real applications.
- Learn the importance of evaluation, tracing, limits, and fallback thinking when developers build modern AI systems.
FULL-STACK
Full-Stack Developers
Ideal for developers who want to connect interfaces, APIs, model outputs, and workflow logic into modern AI applications.
UIAPI
FRONTEND
Frontend Engineers Building AI Features
For engineers designing chat interfaces, AI-native UX, streaming responses, and user-facing workflows that feel practical and product-ready.
CHATUX
SOFTWARE ENGINEERING
Software Engineers Moving into AI Products
A strong fit for engineers who already build software and now want practical exposure to RAG, model APIs, agent logic, and modern AI development patterns.
✓
✓
✓
LLMTOOLS
RAG & WORKFLOWS
Web and App Developers Exploring RAG and AI Workflows
Useful for developers who want to move beyond standard app logic and start building retrieval-backed experiences, AI workflows, and smarter user-facing systems.
RAGAGENTS
PRODUCT TEAMS
Technical Teams Adding AI into Product Experiences
Useful for product-focused technical teams that want a shared understanding of how AI features, workflows, retrieval systems, and model integrations fit into real applications.
✓
APPAI
Skills You’ll Gain as an AI Developer
A practical view of what you’ll be able to build, connect, evaluate, and improve as you work with modern AI application patterns.
Build Retrieval-Backed AI Features
Create modern AI functionality that fits inside real software products
- Build AI features for chat, search, extraction, summarization, and structured workflows
- Work with retrieval-backed responses instead of relying only on raw model outputs
- Design user-facing AI experiences that connect product interfaces with practical AI behavior
Work with Models, Tools, and Structured Outputs
Make AI workflows more usable, grounded, and reliable for application development
- Integrate model APIs, tool-connected workflows, and structured outputs into application logic
- Use retrieval, schema-based inputs and outputs, and practical safeguards for stronger reliability
- Understand how developers work with prompts, tools, actions, and response control in modern AI systems
Develop Practical AI Systems with Testing and Iteration
Improve quality, safety, and application behavior through a stronger engineering workflow
- Think in terms of testing, tracing, evaluation, and iterative improvement instead of one-shot demos
- Work with privacy, safety, guardrails, and basic failure-aware development patterns
- Build portfolio-ready AI projects that reflect practical implementation and improvement thinking
Want the complete skill checklist?
Expand to see the major capability areas covered in one glance.
View
Want the complete skill checklist?
Expand to see the major capability areas covered in one glance.
Build retrieval-backed AI features
Integrate model APIs reliably
Work with structured prompts and outputs
Ground AI behavior with documents and retrieval
Design tool-connected AI workflows
Apply safety, privacy, and response controls
Evaluate quality and track improvement
Create practical, portfolio-ready AI builds
Tools, Frameworks, and Models You’ll Work With
A practical developer-first AI stack covering model APIs, retrieval workflows, agent frameworks, application building, and evaluation.
Model APIs
Chat, embeddings, streaming, and tool-connected interactions
LangChain
coreApplication patterns for tools, memory, and workflow building
LlamaIndex
Document pipelines and retrieval-oriented development blocks
The course focuses on practical development patterns first, so tools can be understood in context instead of as a random stack list.
Vector DB
Semantic search with metadata filtering across stores such as Qdrant, Pinecone, and Chroma
Embeddings
Turn text and documents into retrievable meaning for modern AI applications
The course focuses on practical development patterns first, so tools can be understood in context instead of as a random stack list.
LangGraph
advancedReliable multi-step agent workflows with routing, retries, and control
MCP Patterns
Consistent tool and context integration patterns across AI applications
The course focuses on practical development patterns first, so tools can be understood in context instead of as a random stack list.
FastAPI
Backend APIs for practical AI applications and integrations
Gradio / Streamlit
Fast interfaces and prototypes that help developers ship working AI experiences
The course focuses on practical development patterns first, so tools can be understood in context instead of as a random stack list.
Vercel / AWS
Deployment, environment setup, and ongoing delivery workflows
GitHub
Version control, collaboration, and portfolio-grade code management
The course focuses on practical development patterns first, so tools can be understood in context instead of as a random stack list.
LangSmith
coreTracing, prompt versions, datasets, and evaluation runs
RAGAS
coreEvaluate retrieval quality and answer quality in RAG applications
DeepEval
Automated checks for output quality, consistency, and reliability
The course focuses on practical development patterns first, so tools can be understood in context instead of as a random stack list.
AI Developers Course Curriculum
11 sections|36+ modules|Projects hands-on|ILT mentor-led
Sample certificate preview
Industry-Recognized Certificate
Upon completing the AI Developer Course, you'll receive an industry-recognized certificate from the School of Core AI—validating your expertise in GenAI, LLMs, RAG, Agentic AI, and production deployment.
CERTIFICATE
OF ACHIVEMENT
THIS IS TO CERTIFY THAT
SCHOOL
OF
CORE
AI
SHWETHA SHARMA
Date : 25th Jan 26
Has Successfully Completed The
12-Month Comprehensive Generative AI Training Program
Conducted By The School Of Core AI.
This Intensive Program Included Hands-On Training In Python, Data Structures, Git, SQL, Docker, Machine Learning, Deep Learning, And Advanced Generative AI Technologies Such As LLMs, VLMs, Stable Diffusion, And Prompt Engineering.
Aishwarya Pandey
Founder and CEO
Certification ID :
SHWETASHARMA250126
SCHOOL
OF
CORE
AI
This certificate validates your expertise in building production-ready AI applications with LLMs, RAG pipelines, Agentic AI, and multimodal systems.
AI Developer Course Fees
Admissions open•Next batch: 15th–30th
One-time payment
₹40,000
3 months • Live ILT • Capstone • Certificate
All-inclusive
3 months
Live ILT
4 projects + capstone
Verifiable cert
No hidden charges. Batch timings confirmed on call.
AI Developer Course fees are 40,000 INR for a 3 month live instructor-led training program with weekday and weekend options, guided projects, capstone demo, and verifiable certificate.
AI Developer Career Outcomes and Salary Trends
This AI Developer Course is designed to help software developers move toward practical AI application roles by building stronger skills in retrieval workflows, model APIs, AI agents, application logic, and real implementation patterns used in modern products.
Roles You Can Target After the Course
- • AI Developer
- • AI Application Developer
- • GenAI Application Developer
- • RAG Developer
- • AI Product Engineer
- • LLM Application Developer
What Usually Influences Salary Growth
- • Depth in backend development, APIs, and practical application building
- • Ability to work with RAG, model APIs, and AI workflow design
- • Strong project execution, debugging, evaluation, and delivery quality
- • Portfolio strength, interview performance, and product-level implementation ability
How to Read Salary Trends Realistically
Salary trends vary widely based on location, company type, prior engineering experience, cloud exposure, product complexity, and how well you can demonstrate practical AI work. Developers with stronger real-world builds, clearer implementation thinking, and better system understanding generally position themselves more strongly.
Career outcomes depend on your prior development experience, the strength of your portfolio, interview performance, and the type of AI application work you can demonstrate. If you later want to publish exact salary ranges, use verified and current sources.
What Developers Say
Real outcomes from developers who upskilled and shipped AI-powered features — without the hype.
Backend Developer → Now building AI-backed APIs
Aman Sharma
Systems > prompts
“I had tried LLM APIs earlier, but only for small experiments. Here I understood how to structure an AI feature like a real backend service — retrieval, evaluation checks, and fallback behavior. The shift was thinking in systems, not prompts.”
Outcome: Built and shipped practical AI features
Frontend Developer → Now ships AI UI features
Priya Nair
Streaming UX
“Earlier I could call an API and show output. Now I understand streaming responses, grounding answers with sources, and handling edge cases in the UI. It finally feels like a product feature, not a demo screen.”
Outcome: Built and shipped practical AI features
Full-Stack Developer → Now integrates AI into apps
Rohit Singh
Production mindset
“The big learning was production thinking — rate limits, retries, logs, and cost tracking. Before this, AI felt unpredictable. Now I know how to make it reliable enough to ship inside real user flows.”
Outcome: Built and shipped practical AI features
Software Developer → Now builds retrieval workflows
Mehul Patel
Retrieval that works
“I moved from basic automation scripts to building retrieval-based workflows that solve real tasks. The architecture breakdown helped me see where things fail in production and how to design around it.”
Outcome: Built and shipped practical AI features
Product-Focused Developer → Now prototypes AI faster
Emily Carter
Product thinking
“I already worked with APIs, but this helped me understand how AI changes product design — latency, uncertainty, and user trust. That perspective was extremely practical.”
Outcome: Built and shipped practical AI features
Software Engineer → Now designs safe workflows
Daniel Hughes
Guardrails + tracing
“AI started feeling like normal software engineering. Instead of treating models like magic, I learned how to wrap them with validation, guardrails, and observability so teams can actually rely on it.”
Outcome: Built and shipped practical AI features
AI Developer Course vs Other AI Tracks
This AI Developer Course is designed for software developers who want to build modern AI applications using APIs, RAG workflows, model integrations, and practical development patterns. If you are comparing different AI courses, this section helps you understand which path fits your goals better.
In simple terms: choose the AI Developer Course if your main goal is to build AI-powered product features and application workflows, not just study AI concepts in isolation.
AI Developer Course
Best fit for this pageBest for software developers who want to build modern AI applications using APIs, RAG workflows, model integrations, and practical application logic.
Best for
Backend, frontend, and full-stack developers building AI features inside products.
Main focus
AI app development, retrieval workflows, model APIs, agent patterns, and practical software implementation.
You are viewing this track
Generative AI Course
Better suited for learners who want broader Generative AI understanding across concepts, workflows, multimodal use cases, and wider implementation patterns.
Best for
Learners who want broader GenAI coverage beyond software application building alone.
Main focus
GenAI concepts, prompting, multimodal workflows, use cases, and broader implementation understanding.
Agentic AI Course
Best for learners who want a stronger focus on AI agents, multi-step workflows, tool-connected systems, and orchestration-heavy application patterns.
Best for
Developers and builders exploring agent-first systems and workflow orchestration.
Main focus
AI agents, tool use, planning logic, orchestration, and multi-step execution patterns.
LLM Mastery Program
Best for learners who want deeper exposure to large language models, transformer foundations, model behavior, and LLM-focused understanding beyond app-building alone.
Best for
Learners who want deeper model-level understanding and LLM specialization.
Main focus
LLM concepts, transformer understanding, model behavior, optimization thinking, and deeper LLM-focused learning.
AI Developer Course FAQs
Quick answers to the most common questions software developers ask before joining.
Got More Questions?
Talk to Our Team Directly
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