Deesha AI · SkillLab
AI App Engineering Lab
Two hands-on days turning a language model into something users can actually use: model APIs, structured outputs, streaming, tool calling and an end-to-end AI feature inside a real application, with the failure handling and cost controls that make it shippable.
Designed for
- Developers who have called an LLM API and want to ship a real feature
- Backend, frontend and full-stack engineers being asked for AI features
- Technical leads deciding how AI lands in their product
- Final-year and advanced CS learners with working code behind them
- Fee
- ₹2,700
- Duration
- 2 days · 16 hours
- Each day
- 09:00 – 17:00
- Mode
- Offline / Online
The 2 days, hour by hour
10 hands-on sessions: every one ends in a thing
Day 1
09:00 – 17:00Understand and integrate
5 sessions09:00 – 10:15
See what a model call really is
the modern AI app stack, tokens and context, model selection trade-offs
Takeaway Three models compared on one task: latency, cost and quality on one table you made.
10:30 – 11:45
Design prompts and messages like an engineer
system vs user intent, message design, prompt assets vs chat
Takeaway A prompt rewritten three ways with the output diffed, the vague version retired for good.
12:00 – 13:00
Constrain the output with a schema
structured outputs, validation, retry-on-invalid
Takeaway Model responses bound to typed, validated objects: a rename breaks the build, not production.
14:00 – 15:15
Stream it into a real interface
streaming responses, conversation state, error handling
Takeaway A streaming AI endpoint in your own application, surviving a dropped connection.
15:30 – 17:00
Build the first working feature
wiring it together, the happy path, the first failure path
Takeaway Your first end-to-end AI feature running: small, structured and testable.
Day 2
09:00 – 17:00Make it application-ready
5 sessions09:00 – 10:15
Give the model tools
function calling, tool schemas, external APIs
Takeaway The model completing a task by calling your functions, with the call log read aloud.
10:30 – 11:45
Constrain what it may do
boundaries, human approval, side effects
Takeaway A consequential action gated behind explicit approval, demonstrated by refusing one.
12:00 – 13:00
Handle failure like production
rate limits, retries, timeouts
Takeaway The feature surviving a provider outage, injected live: degrading, not dying.
14:00 – 15:15
Control the cost
token budgets, caching, cost ceilings
Takeaway A cost ceiling that actually stops spend, proved against a runaway loop.
15:30 – 17:00
Integrate, demo and defend
end-to-end integration, the demo, design review
Takeaway Your feature demoed end to end and reviewed, with the design notes you take home.
By the end of day 2, you are holding
A working end-to-end AI feature inside an application in your own stack (structured outputs, streaming, tool calling, failure handling and a cost ceiling), plus the design notes to defend it in review.
No dates announced
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Who runs it
Practitioners, in the room with you
All trainers and mentorsEach workshop names its trainer before you book.
Vishal Shah
Founder & Principal Trainer
Two decades building and teaching commerce, banking and cloud platforms, and still writing code
- Java & Spring Boot microservices
- TypeScript, React, Angular & Next.js
- Composable commerce (commercetools)
Shrenik Shah
Principal Trainer
Cloud and AI architect who has upskilled over 5,000 engineers in Java, React, Python and cloud
- Python: Django, Flask, GenAI & agentic workflows
- Java & Spring Boot
- React & Angular, micro-frontend architecture
Running this for a team?
We deliver SkillLabs on site for institutions and engineering teams.
₹2,700
2 days · 16 hours