Programme Parameters
Advanced Postgrad
4 Weeks (40 Hrs)
Online (e-LMS)
• Prior working knowledge of the field and comfort with core tools and quantitative reasoning.
• A laptop or desktop with a stable internet connection.
• Willingness to complete assignments and the capstone project.
About This Course
Generative AI & LLMs Practicum for Science is a hands-on course on putting generative AI to work in research. You practise using large language and generative models across the scientific workflow — synthesising literature, assisting analysis and coding, generating hypotheses and drafting communication — and learn the guardrails science demands: accuracy, reproducibility and integrity. The emphasis is practical, responsible application to real scientific tasks. You finish able to use generative AI to accelerate your scientific work. A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
This practicum covers generative AI and LLMs for science — using generative models to accelerate scientific research, analysis and communication, applied hands-on.
📋 Course Objectives
1. Use LLMs to synthesise literature.
2. Assist analysis and coding with generative AI.
3. Support hypothesis generation.
4. Draft scientific communication.
5. Uphold accuracy and research integrity.
👥 Who Should Enroll?
• Researchers and PhD scholars
• Scientists across disciplines
• Research-support professionals
• Students in science
🚀 Key Learning Outcomes
• Practical generative AI for science.
• A responsible research-AI workflow.
• Accelerated scientific productivity.
• A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
💎 What You'll Gain
Live & Recorded Sessions
Lifetime access to class recordingse-Certificate on Completion
Cryptographically verified credentialPost-Programme Support
Direct access to mentors & councilHands-On Experience
Notebooks, real-world code & datasetsCurriculum Outline
Foundations of Generative AI & LLMs for Science
Understand how Generative AI and Large Language Models support scientific research, learning, analysis, and documentation. • Learn key concepts such as prompts, tokens, context windows, model outputs, hallucination, reasoning, retrieval, and AI-assisted workflows. • Explore practical uses of LLMs in biotechnology, healthcare, chemistry, materials science, environmental science, and data-driven research.
Prompt Engineering for Scientific Workflows
Design clear and structured prompts for scientific explanations, summaries, comparisons, and research planning. • Use role-based prompting, structured instructions, examples, constraints, and output formats for better AI responses. • Build reusable prompt templates for literature review, hypothesis generation, protocol drafting, and scientific communication.
AI-Assisted Literature Review and Research Mapping
Use Generative AI to summarize research articles, extract key themes, compare studies, and identify research gaps. • Organize scientific knowledge into outlines, tables, concept maps, and structured review notes. • Learn how to validate AI-generated literature summaries using source checking and human review.
Scientific Writing, Reports, and Documentation with LLMs
Draft scientific abstracts, introductions, reports, explanations, lab notes, and technical summaries using AI assistance. • Improve clarity, structure, grammar, readability, and scientific tone without changing the meaning of the content. • Use LLMs for research proposal outlines, presentation scripts, manuscript planning, and documentation workflows.
LLMs for Scientific Data Interpretation
Use AI to interpret tables, experimental observations, analytical summaries, and research datasets. • Convert scientific data into readable explanations, insights, limitations, and decision-support notes. • Understand the role of human validation when using AI for scientific data interpretation and reporting.
Retrieval-Augmented Generation for Scientific Knowledge
Understand how Retrieval-Augmented Generation helps connect LLMs with trusted scientific documents and knowledge bases. • Explore how RAG supports literature search, document Q&A, research summarization, and scientific decision support. • Learn basic workflows for grounding AI responses in reliable scientific sources and reducing hallucination risks.
AI for Experiment Planning and Scientific Problem Solving
Use Generative AI to plan experiments, prepare checklists, design workflows, and identify possible limitations. • Generate structured research questions, hypotheses, variables, controls, and expected observations. • Apply LLMs to simplify complex scientific concepts and support interdisciplinary problem solving.
Responsible AI, Research Ethics, and Quality Control
Understand hallucination, bias, privacy, plagiarism, citation misuse, data sensitivity, and responsible AI practices in science. • Learn how to fact-check AI outputs, verify scientific claims, and maintain academic integrity. • Apply human review, source validation, and ethical documentation when using AI in scientific workflows.
Capstone: End-to-End Generative AI & LLMs Science Practicum Project
Work on a complete science-focused AI workflow involving literature review, prompt design, data interpretation, and report creation. • Create AI-assisted outputs such as summaries, research maps, experiment plans, documentation, and presentation material. • Build a portfolio-ready practicum project demonstrating responsible and practical use of Generative AI and LLMs in science.
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | Generative AI |
| Covered Tool / Platform | Large Language Models |
| Covered Tool / Platform | Prompt Engineering |
| Covered Tool / Platform | Scientific Writing |
| Covered Tool / Platform | Literature Review |
| Covered Tool / Platform | Research Mapping |
| Covered Tool / Platform | RAG |
| Covered Tool / Platform | Scientific Data Analysis |
| Covered Tool / Platform | Experiment Planning |
| Covered Tool / Platform | Responsible AI |
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