Generative AI Course — build with LLMs, not just talk about them.
Prompt engineering, RAG pipelines, agents and evals — the practical skills behind real GenAI products.
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50+ founders consulted last month
Anyone can paste a prompt into ChatGPT. This course teaches the actual engineering behind production GenAI — retrieval, evaluation, guardrails and agentic workflows.
Course outcomes
Every module ships with a working tool or template you keep using after class.
Build a RAG pipeline end to end
From document ingestion to vector search to grounded generation.
Design production prompt chains
Structured prompts and function calling that don't break on edge cases.
Set up evals and guardrails
Automated evaluation suites so quality doesn't silently regress.
Ship an AI agent project
A working agentic workflow you can show in interviews or to clients.
Built for builders
Every module ends with something working — a RAG pipeline, an agent, an eval suite — not just slides about what LLMs are.
Who this is for
- Engineers who want to add GenAI to their toolkit
- Technical founders building AI features
- Data/ML practitioners moving into applied GenAI
Quick answer
The Generative AI course is a 6-week, hands-on program teaching prompt engineering, RAG pipelines, agents and evals. It's for engineers, technical founders and data/ML practitioners who want to build production GenAI systems, and it ends with a working RAG or agent project.
How this compares to free prompt engineering tutorials
- Free tutorials mostly cover prompting basics; this course goes into RAG architecture, vector databases and agentic tool use that production systems actually need
- Every module ships something working — a pipeline, an agent, an eval suite — instead of slides about how LLMs work
- Evals and guardrails, the part that separates a demo from a production system, are a dedicated module here and rarely covered in free content
- You leave with a working RAG or agent project you can show, not just notes on concepts
What you'll walk away with
- A complete RAG pipeline built end to end — ingestion, vector search and grounded generation
- Production-grade prompt chains and function calling that hold up on edge cases
- An automated evaluation suite so output quality doesn't silently regress
- A working AI agent project for interviews or client work
- Working knowledge of OpenAI, Anthropic and open-source model APIs
What this generative AI course actually covers
This course goes beyond simply "how to use ChatGPT" to cover the underlying mechanics of large language models, practical prompt engineering as a discipline, retrieval-augmented generation architecture, and the evaluation practices needed to build genuinely reliable applications rather than impressive demos that fall apart in production.
Using AI tools vs building AI applications
| Aspect | Using AI tools | Building AI applications |
|---|---|---|
| Skill required | Effective prompting for personal use | Architecture, evaluation, and production reliability |
| Outcome | Personal productivity gains | Software products others can rely on |
Participants learn the meaningfully different skill set required to build applications for other users, beyond effective personal use of existing AI tools.
Course structure and progression
Common misconception about prompt engineering
Who this course is for
- Developers wanting to build production-grade LLM-powered applications
- Product-minded engineers exploring generative AI feature opportunities
- Technical professionals wanting to move beyond casual AI tool usage
Cost management as a practical engineering skill
Participants learn practical cost management strategies — caching, prompt optimization, model tier selection — treating cost efficiency as a core engineering skill rather than an afterthought addressed only after an unexpectedly large bill arrives.
Handling hallucination and building user trust
A significant part of the course addresses practical mitigation strategies for hallucination — grounding responses in retrieved facts, confidence signaling — recognizing this as a core reliability challenge rather than an occasional edge case to ignore.
Building a portfolio-worthy GenAI application
The capstone project is designed to serve as a genuine portfolio piece, demonstrating a working application with proper evaluation, not just an impressive demo that a technical interviewer or hiring manager can dismiss as superficial.
Prerequisites before starting this course
Basic programming experience is recommended, ideally with some exposure to APIs, though the course reviews foundational concepts before diving into LLM-specific application building. No prior machine learning theory background is required — the focus is on practical application development, not training models from scratch.
Instructor background and teaching approach
Instructors bring direct experience building production GenAI applications, not just familiarity with API documentation, ensuring the practical pitfalls discussed — cost overruns, hallucination handling, latency issues — reflect genuine production experience.
Format: live sessions vs self-paced learning
The course combines live coding sessions and architecture discussions with self-paced material for foundational LLM concepts, balancing flexibility with the hands-on practice needed to genuinely internalize application-building skills.
Group size and individualized feedback
Cohorts are kept intentionally small to ensure each participant receives detailed code review and architecture feedback on their capstone project, rather than generic feedback disconnected from their specific implementation choices.
Certificate and portfolio value after completion
The certificate is accompanied by the completed application and its evaluation pipeline as tangible portfolio evidence, providing concrete proof of capability that a certificate alone cannot demonstrate to a potential employer.
Pricing and payment options
Tuition is presented up front with no surprise charges, and installment plans are offered so cost isn't a barrier for professionals from a wide range of backgrounds.
Support after course completion
Participants retain access to an alumni community where practical questions encountered while building GenAI applications on the job can be asked long after course completion, providing ongoing value beyond the formal curriculum.
Comparing this course to following online tutorials
| Aspect | Online tutorials | This course |
|---|---|---|
| Depth | Often surface-level demos | Production-grade application building |
| Code review | Absent | Detailed feedback on actual implementation |
Participants consistently report that detailed code review on their own capstone application caught issues that following tutorials alone would never have surfaced.
Handling the pace of model releases in the curriculum
Because model capabilities evolve rapidly, curriculum content is reviewed and updated regularly to reflect current model capabilities and best practices, rather than teaching techniques that have since become outdated.
Group pricing for teams and companies
Companies wanting to upskill multiple engineers simultaneously can benefit from group pricing, with the option to adapt project examples to the company's specific tech stack for added relevance.
Remote vs in-person format availability
The course is available both remotely and in-person, with equivalent content and hands-on coding support quality in both formats, allowing participants to choose based on personal preference and logistical constraints.
How this course differs from a general programming bootcamp
| Aspect | General programming bootcamp | This course |
|---|---|---|
| LLM-specific depth | Minimal or absent | Core focus of the entire curriculum |
| Evaluation methodology | Not typically addressed | Central curriculum topic |
Participants who already have programming experience find this course adds genuinely new, LLM-specific skills rather than repeating general programming concepts they've already mastered.
Confidentiality of capstone project content
Participants can base their capstone project on a real application idea from their own company, with appropriate anonymization, and any confidential business details shared during the course are treated with strict discretion.
Career outcomes after completing this course
Graduates commonly move into GenAI engineering roles, lead LLM feature initiatives within existing engineering teams, or build their own AI-powered products as founders. The capstone application frequently becomes the centerpiece of technical interview conversations for these roles.
Balancing technical ambition with production reliability
A recurring theme throughout the curriculum is finding the pragmatic balance between ambitious application ideas and what can be built reliably given current model capabilities, rather than either dismissing GenAI prematurely or overcommitting to unproven reliability.
Networking and community value beyond the curriculum
Cohort-based learning creates a genuine peer network of other GenAI practitioners navigating similar technical challenges, a community that often continues providing value through job referrals and ongoing informal advice long after the formal course concludes.
Final thought on what makes this course different
Many GenAI resources focus on impressive demos showcasing what's theoretically possible. This course focuses instead on the practical engineering judgment needed to ship reliable applications, a genuinely different and more immediately applicable skill set.
Handling AI ethics and responsible application design
Ethical considerations — bias in outputs, appropriate use cases, transparency with users about AI involvement — are woven throughout the curriculum as practical engineering decisions rather than treated as a separate abstract topic disconnected from day-to-day implementation choices.
Working with cross-functional teams as a GenAI engineer
Participants learn practical approaches for collaborating effectively with product managers and designers, translating between technical constraints and product requirements clearly and without unnecessary jargon.
Can this course help with a specific application I'm already building?
Yes — many participants use their capstone project to work through a real application they're already building at their current company, receiving structured feedback with immediate practical application.
Handling participants with varying programming experience levels
Participants with less programming experience are supported with additional foundational material and pairing opportunities, ensuring they can build genuine hands-on skills without feeling overwhelmed by more experienced peers moving at a faster pace.
Is this course updated to reflect evolving model capabilities?
Yes, reviewed regularly to reflect current model capabilities and best practices, ensuring techniques taught always match what's genuinely effective today rather than outdated approaches.
Real-world case studies used throughout the course
Rather than abstract hypotheticals, the curriculum draws on real (anonymized) case studies of GenAI applications that succeeded and failed in production, giving participants concrete examples of the architecture and evaluation decisions that separated the two outcomes.
Handling technical debt and iteration in GenAI applications
Participants learn practical approaches for managing technical debt specific to GenAI applications — outdated prompts, deprecated model versions — recognizing that these applications require different maintenance patterns than traditional deterministic software.
Is there a minimum experience level required to enroll?
Some prior programming experience is recommended, though the course is designed to accommodate a range of backgrounds from junior developers to experienced engineers exploring GenAI for the first time.
Can this course be adapted to my company's specific tech stack?
Yes — while core concepts remain consistent, examples and the capstone project can be tailored to reflect the specific technology stack participants work with.
Handling the transition from feature-focused to reliability-focused thinking
A recurring shift participants undergo during the course is moving from thinking about GenAI in terms of impressive capabilities to thinking in terms of reliability an application must consistently deliver, a subtle but consequential reframing that shapes every subsequent engineering decision.
Confidentiality and support after the capstone presentation
Feedback continues to be available even after the formal capstone presentation, allowing participants to refine their application further as they prepare it for actual job interviews or open-source portfolio publication.
Is the certificate recognized by employers?
The certificate is designed to demonstrate concrete, verifiable skill via the capstone application portfolio rather than relying on name recognition alone, giving employers tangible evidence of practical ability.
Can I retake modules if I need more practice?
Yes — recorded sessions and materials remain accessible after the course, allowing participants to revisit specific modules for additional practice as needed.
Week-by-week breakdown
A clear, transparent syllabus — no surprises.
Foundations of LLMs & prompting
How LLMs actually work, and how to prompt them reliably.
RAG & vector databases
Retrieval-augmented generation with Pinecone, pgvector or Weaviate.
Agents & tool use
Building agents that call tools and complete multi-step tasks.
Evals, guardrails & deployment
Testing, safety and shipping GenAI features to production.
01Do I need Python experience?
Basic Python is recommended — the course focuses on architecture, not syntax.
02Which LLM providers do you cover?
OpenAI, Anthropic (Claude), and open-source models.
03Is this the same as the AI Product Management course?
No — this is hands-on engineering (building pipelines and agents); the PM course is scoping and strategy.
04Do I get a project I can show employers?
Yes — a working RAG or agent project is part of the curriculum.
05How much does this course cost?
₹12,999 for the 6-week Intermediate cohort, covering RAG, agents, evals and hands-on labs with OpenAI, Anthropic and open-source models.
06Is this worth it compared to free ChatGPT/prompting tutorials online?
Free tutorials mostly stop at prompting basics; this course covers RAG architecture, vector databases, evals and agentic workflows — the engineering that separates a demo from a production system.
07Do I need to already know machine learning?
No formal ML background is required, but basic Python is recommended since the course focuses on architecture and implementation, not ML theory.
08Is there a certificate?
Yes, plus a working RAG or agent project in your portfolio, which carries more weight with employers than the certificate alone.
09What if I fall behind during the cohort?
All sessions are recorded, and because each module ships a working artifact, you can rebuild any module at your own pace using the same starter code.
10What roles can this help me move into?
Graduates use this to move into applied AI engineering, add GenAI features to an existing product, or strengthen a technical founder's ability to build AI features in-house.
11Is this course just about using ChatGPT effectively?
No — it covers building applications, including RAG architecture and evaluation pipelines, well beyond effective personal prompting.
12Is prompt engineering just trial-and-error wording?
No — a rigorous approach involves systematic testing and version control, treating prompts as a genuine engineering artifact.
13Is there a hands-on capstone project?
Yes — a working GenAI application with a complete evaluation pipeline built throughout the course.
14Who typically enrolls in this course?
Developers building LLM applications, product-minded engineers exploring GenAI features, and professionals moving beyond casual AI usage.
15Does the capstone serve as a portfolio piece?
Yes — demonstrating a working application with proper evaluation, not just an impressive but superficial demo.
16Do I need prior machine learning theory background?
No — the focus is on practical application development, not training models from scratch.
17Do instructors have real production GenAI experience?
Yes — direct experience building production applications, not just familiarity with API documentation.
18Is the format live or self-paced?
A combination — live coding sessions and architecture discussions with self-paced material for foundational concepts.
19Are installment payment options available?
Yes — splitting tuition into installments is available for exactly that reason.
20Is there support after course completion?
Yes — an alumni community remains accessible for practical questions encountered while building applications on the job.
21Is the curriculum updated as models evolve?
Yes — reviewed regularly to reflect current model capabilities rather than teaching outdated techniques.
22Is group pricing available for companies?
Yes — for upskilling multiple engineers, with the option to adapt projects to the company's specific tech stack.
23Is the course available remotely?
Yes — with equivalent content and hands-on coding support quality in both remote and in-person formats.
24How does this differ from a general programming bootcamp?
LLM-specific depth and evaluation methodology are the core focus, adding genuinely new skills beyond general programming.
25What career outcomes are common after completing this course?
GenAI engineering roles, leading LLM initiatives within existing teams, or building AI-powered products as a founder.
26Does the course focus on impressive demos or production reliability?
Production reliability — the engineering judgment needed to ship applications that work consistently, not just impressive demos.
27Are AI ethics considerations addressed?
Yes — woven throughout as practical engineering decisions like bias handling and transparency, not a separate abstract topic.
28Can this course help with an application I'm already building?
Yes — many participants use their capstone to work through a real current project, receiving feedback with immediate practical application.
29Is support available for less experienced programmers?
Yes — additional foundational material and pairing opportunities support participants at varying experience levels.
30Is the course updated to reflect evolving model capabilities?
Yes, reviewed regularly to reflect current capabilities rather than outdated approaches.
31Are real case studies used in the curriculum?
Yes — anonymized real case studies of applications that succeeded and failed give concrete examples of what separated the two outcomes.
32Is technical debt management for GenAI applications covered?
Yes — practical approaches for managing outdated prompts and deprecated model versions, recognizing different maintenance patterns.
33Is there a minimum experience level required?
Some prior programming experience is recommended, though the course accommodates a range of backgrounds and experience levels.
34Can this be adapted to my company's specific tech stack?
Yes — while core concepts remain consistent, examples and the capstone can be tailored to your specific tech stack.
35Does the course shift thinking from capabilities to reliability?
Yes — a recurring shift from impressive capabilities to the reliability an application must consistently deliver.
36Is feedback available after the capstone presentation?
Yes — continued feedback helps participants refine their application further for interviews or portfolio publication.
37Is there a scholarship or discount for career changers?
Discounts get worked out case by case — a direct conversation is the fastest way to find out what's possible.
38Can I audit the course without completing the capstone?
Certification depends on completing the capstone — that's the piece where the skills actually get proven, not an optional add-on.
39Is the certificate recognized by employers?
The certificate demonstrates concrete, verifiable skill via the capstone portfolio rather than relying on name recognition alone.
40Can I retake modules if I need more practice?
Yes — recorded sessions and materials remain accessible after the course for revisiting specific modules.
41Can I get feedback on a project after the course ends?
Alumni community access allows continued informal feedback exchange with instructors and peers well after the formal course concludes.
42Are open-source and proprietary models both covered?
Yes — both are covered, with practical guidance on choosing between them based on cost, control, and specific use case requirements.
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