Build with the new AI stack
Master Generative AI and ship LLM-powered products that create real value
Learn how to design, evaluate, and deploy applications powered by large language models. From prompt engineering and retrieval workflows to copilots, RAG systems, and production guardrails, this program helps you move from experimentation to reliable product delivery.
- Project-first learning
- Placement support
- Industry mentors
Why AXONTech
Built for real GenAI products, not shallow prompt tricks
Teams adopting generative AI need more than API wrappers. They need builders who understand context windows, retrieval, hallucination risks, guardrails, evaluation, and business fit. This track is designed to help you build that depth so your work feels reliable, not experimental.
Product-first architecture
Learn how LLMs, retrieval, tools, prompts, and evaluation come together inside real user-facing products.
Beyond prompt-only thinking
Build confidence with prompt patterns, retrieval strategies, orchestration, and measurable evaluation methods.
Portfolio with product signal
Create case-study-worthy copilots, assistants, and workflows that are easier for hiring teams to evaluate.
Career support included
Resume support, mock interviews, and placement guidance are built into the full GenAI learning journey.
What you get
A structured generative AI track built around modern LLM delivery
The concepts, tooling, and evaluation habits needed to move from experimentation to dependable GenAI products.
LLM foundations
Understand transformers, tokens, embeddings, context windows, and what makes LLM systems behave the way they do.
Prompt engineering and control
Design prompts, instructions, role constraints, and response patterns that improve reliability and clarity.
RAG and context systems
Build assistants that use your own documents, knowledge bases, and retrieval pipelines with better grounding.
APIs, frameworks, and orchestration
Use leading GenAI APIs and development patterns to connect LLMs to real application workflows.
Responsible AI and evaluation
Build with safety checks, moderation patterns, evaluation criteria, and guardrails that reduce risk.
Deployable portfolio projects
Create GenAI case studies that show product thinking, technical depth, and measurable outcomes employers care about.
Structured learning path
What you'll learn
A six-part roadmap that takes you from LLM fundamentals to real-world GenAI systems, evaluation, and product delivery.
Generative AI and LLM basics
Generative models, tokens, embeddings, transformers, and the foundations behind modern language-model systems.
Prompt design and patterns
System prompts, role framing, few-shot examples, chaining, and ways to improve consistency and control.
Retrieval-augmented generation
Embeddings, vector search, chunking, retrieval strategy, and grounding assistants in enterprise-style knowledge.
Agents, tools, and orchestration
Connect models to external tools, structured actions, workflows, and step-by-step product logic.
GenAI application design
Build copilots, chat interfaces, document assistants, and content systems with clearer product thinking.
Evaluation, safety, and production readiness
Guardrails, hallucination checks, moderation, monitoring, and practical patterns for reliable rollout.
Where this leads
Roles this program prepares you for
Generative AI is creating new product, platform, and engineering roles that value builders who understand both capability and control.
Build and evaluate LLM-powered features, assistants, and product-facing AI experiences.
Design prompt flows, retrieval systems, and API integrations that support real users and workflows.
Build assistants for support, internal knowledge, workflows, and employee productivity use cases.
Apply GenAI to marketing, operations, documentation, and internal systems where speed and leverage matter.
Market context
Why generative AI is transforming how modern teams build and work
Generative AI is changing how organizations create content, search knowledge, automate workflows, and design software-assisted experiences.
Teams use GenAI to ship support assistants, content systems, and productivity features faster than before.
Companies want people who understand both what LLMs can do and where they can fail in production.
Strong GenAI case studies give employers clear evidence of technical depth and product thinking.
GenAI spans engineering, operations, support, content, internal knowledge, and customer-facing experiences.
Hands-on portfolio
Real-world generative AI projects with product-level relevance
Work on projects that go beyond one-off demos. Each build is designed to help you think like a team shipping an LLM-powered product, with clearer constraints, evaluation criteria, and business value.
- Knowledge-base assistant using RAG over organization-style documents, FAQs, and policy content
- Content or marketing copilot that drafts, rewrites, summarizes, and adapts messaging with workflow controls
- Developer or operations copilot prototype that explains code, summarizes context, and supports task execution
Stack you'll touch
Tools and frameworks
A practical GenAI stack that helps you go from prompt design to retrieval, orchestration, evaluation, and deployment habits.
Support & outcomes
Placement and career systems
Generative AI attracts attention fast, but strong architecture thinking, project framing, and interview readiness turn that attention into opportunity. This track is designed to support both sides.
Placement assistance
Position your GenAI portfolio for product, platform, and innovation roles with stronger role targeting and outreach strategy.
GenAI portfolio reviews
Shape assistants, copilots, and LLM products into clearer case studies with visible business outcomes.
Technical and system interviews
Practice architecture, retrieval design, evaluation, and prompt-system conversations with sharper feedback.
Live job support
Get support while rolling out prompts, retrieval systems, and GenAI workflows in real team environments.
Project sprints
Keep your profile fresh with focused GenAI builds that show ongoing learning and stronger execution quality.
Industry mentorship
Learn from practitioners building production GenAI systems so your thinking stays grounded in modern delivery reality.
