01
Generative AI foundations
- AI, ML and generative AI landscape
- How transformers and LLMs work
- Tokens, context windows and model behaviour
- Choosing models for business use cases
All technology programs
Webshala technology program
Generative & Agentic AI
Design intelligent systems that reason, use tools and complete real work.
A practical learning path covering modern generative AI, LLM application design and production-ready AI agents. Learners move from prompt fundamentals to RAG, tool use, workflow orchestration, evaluation and responsible deployment.
Learning outcomes
What learners will be able to do.
The program is built around observable capability—not passive content completion.
01
Understand how LLM applications and AI agents work
02
Design effective prompts and structured outputs
03
Build grounded applications with RAG and enterprise data
04
Create agents that use APIs, tools, memory and workflows
05
Evaluate, secure and deploy practical AI solutions
Detailed curriculum
From foundations to real implementation.
Modules can be adapted to the learner profile, available hours and institutional or team outcomes.
02
Prompt engineering
- Prompt anatomy and instruction design
- Zero-shot, few-shot and chain-based techniques
- Structured outputs and reusable templates
- Testing prompts for accuracy and consistency
03
Building LLM applications
- Model APIs and application architecture
- Conversation state and context management
- Embeddings and semantic search
- Cost, latency and quality trade-offs
04
RAG and enterprise knowledge
- Document ingestion and chunking
- Vector databases and retrieval
- Grounded response generation
- Improving relevance and reducing hallucinations
05
Agentic AI systems
- Agent planning and reasoning patterns
- Tool calling and API integration
- Memory and stateful workflows
- Human-in-the-loop approvals
06
Multi-agent workflows
- Specialist-agent design
- Delegation and orchestration
- Shared context and hand-offs
- Failure handling and observability
07
Evaluation, safety and governance
- Quality and reliability metrics
- Prompt injection and data protection
- Guardrails and responsible AI
- Monitoring production behaviour
08
Capstone implementation
- Use-case discovery and solution design
- End-to-end agent build
- Testing, documentation and demo
- Deployment and improvement roadmap
Hands-on work
Projects learners can explain and demonstrate.
Every project includes planning, implementation, testing, documentation and a final walkthrough.
Enterprise knowledge assistant
Build a grounded assistant that answers from approved institutional or company documents.
AI operations agent
Create an agent that interprets a request, uses tools and completes a multi-step workflow.
Multi-agent business workflow
Orchestrate specialist agents for research, analysis, review and final output.
TOOLS & PLATFORMS
OpenAI-compatible APIsPython or JavaScriptVector databasesn8nAgent frameworksGit
Who this is for
Built for the right starting point.
- Students and fresh graduates
- Developers and technical teams
- Business analysts and automation professionals
- Faculty and innovation teams
Included in delivery
Support beyond the syllabus.
- Live sessions with working professionals
- Guided labs, assignments and assessments
- Project reviews and implementation feedback
- Certification exam preparation
- Interview questions and role preparation
- Resume and project-presentation guidance
Build your cohort
Want this program for your students or team?
We'll adapt the curriculum, depth and project context to your goals.