FAQs: Generative AI

Basic Python is recommended — the course focuses on architecture, not syntax.

OpenAI, Anthropic (Claude), and open-source models.

No — this is hands-on engineering (building pipelines and agents); the PM course is scoping and strategy.

Yes — a working RAG or agent project is part of the curriculum.

₹12,999 for the 6-week Intermediate cohort, covering RAG, agents, evals and hands-on labs with OpenAI, Anthropic and open-source models.

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.

No formal ML background is required, but basic Python is recommended since the course focuses on architecture and implementation, not ML theory.

Yes, plus a working RAG or agent project in your portfolio, which carries more weight with employers than the certificate alone.

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.

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.

No — it covers building applications, including RAG architecture and evaluation pipelines, well beyond effective personal prompting.

No — a rigorous approach involves systematic testing and version control, treating prompts as a genuine engineering artifact.

Yes — a working GenAI application with a complete evaluation pipeline built throughout the course.

Developers building LLM applications, product-minded engineers exploring GenAI features, and professionals moving beyond casual AI usage.

Yes — demonstrating a working application with proper evaluation, not just an impressive but superficial demo.

No — the focus is on practical application development, not training models from scratch.

Yes — direct experience building production applications, not just familiarity with API documentation.

A combination — live coding sessions and architecture discussions with self-paced material for foundational concepts.

Yes — splitting tuition into installments is available for exactly that reason.

Yes — an alumni community remains accessible for practical questions encountered while building applications on the job.

Yes — reviewed regularly to reflect current model capabilities rather than teaching outdated techniques.

Yes — for upskilling multiple engineers, with the option to adapt projects to the company's specific tech stack.

Yes — with equivalent content and hands-on coding support quality in both remote and in-person formats.

LLM-specific depth and evaluation methodology are the core focus, adding genuinely new skills beyond general programming.

GenAI engineering roles, leading LLM initiatives within existing teams, or building AI-powered products as a founder.

Production reliability — the engineering judgment needed to ship applications that work consistently, not just impressive demos.

Yes — woven throughout as practical engineering decisions like bias handling and transparency, not a separate abstract topic.

Yes — many participants use their capstone to work through a real current project, receiving feedback with immediate practical application.

Yes — additional foundational material and pairing opportunities support participants at varying experience levels.

Yes, reviewed regularly to reflect current capabilities rather than outdated approaches.

Yes — anonymized real case studies of applications that succeeded and failed give concrete examples of what separated the two outcomes.

Yes — practical approaches for managing outdated prompts and deprecated model versions, recognizing different maintenance patterns.

Some prior programming experience is recommended, though the course accommodates a range of backgrounds and experience levels.

Yes — while core concepts remain consistent, examples and the capstone can be tailored to your specific tech stack.

Yes — a recurring shift from impressive capabilities to the reliability an application must consistently deliver.

Yes — continued feedback helps participants refine their application further for interviews or portfolio publication.

Discounts get worked out case by case — a direct conversation is the fastest way to find out what's possible.

Certification depends on completing the capstone — that's the piece where the skills actually get proven, not an optional add-on.

The certificate demonstrates concrete, verifiable skill via the capstone portfolio rather than relying on name recognition alone.

Yes — recorded sessions and materials remain accessible after the course for revisiting specific modules.

Alumni community access allows continued informal feedback exchange with instructors and peers well after the formal course concludes.

Yes — both are covered, with practical guidance on choosing between them based on cost, control, and specific use case requirements.