Overview: Generative AI Course in Technopark

Built for Technopark builders

Every module ends with something working — a RAG pipeline, an agent, an eval suite — built live alongside peers from Technopark and beyond. This goes past prompting into the actual engineering behind production GenAI.

Curriculum breakdown

Module 1: Prompt engineering and structured chains

Technopark students learn structured prompt design and function calling, not just one-off prompt tricks.

Topics covered

• Prompt architecture and function calling
• Structured output validation
• Multi-step prompt chains

Module 2: RAG pipelines with vector databases

Building a working retrieval-augmented generation pipeline, end to end, as a Technopark student.

Topics covered

• Chunking and ingestion strategy
• Vector database selection and indexing
• Retrieval quality evaluation

Module 3: Agents and tool use

Building an agent that uses tools reliably, for Technopark students moving beyond single-turn prompting.

Module 4: Evals, guardrails and deployment

Shipping what you've built with monitoring and safety guardrails in place.

How the cohort runs

Format and schedule

Live, project-based cohort

Fully online, live over Zoom, for Technopark learners and peers across every module.

Who this course is for

Engineers and builders

Technopark engineers who want the architecture behind production GenAI, not just API basics.

Technical founders

Technopark founders building an LLM-powered product who need to move past a fragile prototype.

Outcomes and support

Portfolio project

Technopark students leave with a working RAG or agent project they can show employers.

Prerequisites

Basic Python recommended

The course focuses on architecture decisions for Technopark students, not syntax fundamentals.

Note

Covers OpenAI, Anthropic (Claude), and open-source models, so Technopark students aren't locked into one provider's mental model.

Quick answer

The Generative AI Course in Technopark is a hands-on, project-based program covering prompt engineering, RAG pipelines, agents and evals — the engineering behind production LLM systems, not just prompting tricks. It's built for Technopark engineers and technical founders who want to move a fragile prototype into something production-ready, and it's worth it if your goal is a working portfolio project, not just theoretical understanding of how LLMs work.

How this compares to free YouTube tutorials or API documentation

• You build a working RAG pipeline, agent and eval suite during the cohort, not just watch someone else build one
• Covers OpenAI, Anthropic (Claude) and open-source models together, so Technopark students aren't locked into one provider's documentation and mental model
• Live feedback on your architecture decisions from a practitioner shipping production GenAI systems, not a comments section
• Structured progression from prompting through RAG, agents, evals and deployment, instead of scattered standalone tutorials

Why Technopark builders choose Deepak Suhag

Taught by someone who ships production GenAI systems for clients — not a course built from documentation alone.