FAQs: Generative AI Course in Mawlong

No — prompting is covered but the focus is on RAG, evaluation, safety guardrails and cost engineering needed for production-ready GenAI features.

No, though basic programming experience is assumed — the course teaches GenAI-specific engineering from the ground up.

You'll build a working capstone GenAI feature with evaluation and guardrails, demonstrating production-level engineering.

Graduates typically move into GenAI engineering, AI product development or applied AI roles, with a real capstone project as proof of work.

No — a well-engineered system with a smaller model and good grounding often outperforms a raw call to the most expensive model available.

OpenAI, Anthropic and open-source models, vector databases for retrieval, and evaluation frameworks used in real production GenAI work.

Documentation explains how a model API works; this course teaches the reliability, safety and cost engineering needed to make that call production-ready.

Roughly 6-8 hours, including hands-on build exercises.

How documents get chunked and embedded, what similarity search approach is used, and how retrieved context combines with a user's query — treating RAG as engineering, not a black box.

By building evaluation sets covering realistic edge cases, defining clear criteria for good versus bad responses, and tracking results over time as a repeatable process.

It's when crafted input makes an AI system ignore its original instructions — defenses include input validation, output filtering, and architectural limits on what a compromised interaction can access.

Yes — free tutorials often stop at basic API calls or prompting tricks, without the reliability, safety and cost engineering that separates a demo from a production feature.

Deepak Suhag directly, drawing on real GenAI engineering and production deployment experience.

Yes — the course builds GenAI-specific engineering skills on top of general programming experience, which most backend engineers already have.

Core engineering principles are taught generally, with examples drawn from various use cases relevant to different Mawlong student backgrounds.

The capstone project can be applied to improving an existing feature's reliability, safety or cost profile rather than starting from scratch.

The course focuses on durable engineering principles — evaluation, guardrails, cost management — that remain relevant even as specific model versions change.

Yes, though the stronger credential is the working capstone project demonstrating real production-minded GenAI engineering skill.

Testing only happy-path inputs and assuming readiness for production, or ignoring cost implications until a surprising bill arrives after launch.

It focuses on transferable engineering principles — evaluation design, guardrails, cost management — that remain relevant regardless of which specific model is current.

A capstone GenAI feature with evaluation results and documented guardrails — a genuine engineered system you can explain and defend in technical detail.

The course covers setting realistic expectations about scope and failure modes before launch, since a well-engineered feature can still be perceived as a failure if expectations were unlimited.

Yes — recognizing when a simple rule-based or template-driven approach solves a problem more reliably and cheaply is covered directly, avoiding the trap of using AI just because it's novel.

Clarifying what counts as a good answer and how failure should be handled before building anything is covered directly, avoiding discovering ambiguity only after launch.

Fine-tuning is covered conceptually and compared against prompting and RAG, though most of the course focuses on the more commonly applicable prompting-plus-RAG approach.

Yes — the core engineering principles transfer across industries; your capstone project can be tailored to a use case relevant to your interests.

Yes — the alumni community continues, with Mawlong graduates sharing real-world GenAI engineering challenges they encounter.

Live sessions include troubleshooting time, and the alumni community provides ongoing support for questions after class ends.

This course is practically focused and far shorter, aimed at building applied, production-ready GenAI engineering skills quickly rather than broad theoretical AI education.

Yes — designing systems that work within model constraints, including document chunking for RAG and managing conversation history properly, is covered directly.

Streaming shows partial AI responses as they're generated rather than waiting for completion, which improves perceived performance even when total response time is identical.

Yes — demonstrating a working, evaluated GenAI feature is a concrete way to make the case for further investment in AI engineering capability.

Basic awareness of bias and fairness considerations is woven into the evaluation module, though this course isn't a deep dive into AI ethics specifically.

Core engineering principles transfer across providers — the specific model or API matters less than understanding the underlying evaluation, safety and cost concepts.

Yes — the combination of engineering skill and production-readiness focus is directly applicable to freelance or consulting GenAI development work.

Yes — maintaining reliability across longer conversations, including context tracking and avoiding quality degradation, is covered since many real features involve ongoing dialogue.

Yes — setting realistic expectations about how long production-ready GenAI engineering takes, beyond an initial demo, is discussed directly.

Yes — the capstone project with evaluation and guardrails directly supports the kind of technical questions common in GenAI engineering interviews.

The transferable engineering foundation built here makes learning any specific framework's tooling and conventions significantly faster afterward.

Python is used for exercises given its dominance in the AI ecosystem, though the underlying engineering concepts transfer to other languages as needed.