School of Scientific Computing & Informatics · Data science & AI
Large Language Models for Life Sciences
Large language models can speed up literature review, data curation and coding, and they can also produce confident errors. This course teaches how to use them productively and verify what they produce.
Course overview
Large Language Models for Life Sciences explains how LLMs work at the level a scientist needs, then applies them to real research tasks: searching and summarising literature with citations, extracting structured data from papers, and assisting with analysis code.
Every application is paired with an evaluation: measuring accuracy against a labelled set, detecting fabricated references, and deciding when a human must check the output. Privacy, data protection and institutional policies are covered throughout.
Learning outcomes
By the end of the course you will be able to:
- Explain how LLMs generate text and why they hallucinate
- Write effective, testable prompts for research tasks
- Build retrieval-augmented question answering over papers with citations
- Extract structured data from literature and measure its accuracy
- Use coding assistants safely and set responsible-use rules
Skills you will practise
Eligibility and prerequisites
Who can apply
- Students and graduates in any science, engineering, pharmacy, medicine, agriculture, geography or computing programme (B.Sc., B.Tech., B.E., BCA, M.Sc., M.Tech., MCA, PhD)
- Researchers, faculty and working professionals in research, healthcare, biotech, pharma or IT who want to apply computing to science
- Final-year students may apply before their results are declared
Prerequisites
- Python basics
Best suited to
- Researchers and students who read and curate literature
- Bioinformaticians building research assistants
- Research managers setting AI policies
Module-wise syllabus
6 modules. Each pairs instructor-led sessions with a hands-on lab on public data.
Topics
- Tokens, training and context windows
- Why models hallucinate
- Open versus hosted models
Topics
- Instructions, examples and formats
- Structured outputs
- Prompt testing
Topics
- Embeddings and vector search
- Retrieval-augmented generation
- Citations and grounding
Topics
- Extracting entities and values
- Normalising to ontologies
- Accuracy measurement
Topics
- Assisted coding and review
- Privacy and confidential data
- Writing a responsible-use policy
Topics
- Task
- Build
- Evaluation
Practical sessions and projects
Capstone project
Build a verified assistant
Build an LLM-based tool for a real research task, measure its accuracy against labelled data and document when its output must be checked.
Assessed on the correctness of the analysis, the quality of the code and the reasoning in the write-up. A pass is required for the certificate.
- How LLMs work
Probe a model with questions of known answer and catalogue its failure modes.
- Prompting as engineering
Build and test prompts that classify abstracts by study type.
- Retrieval over literature
Build a question-answering tool over a set of open-access papers that cites its sources.
- Extraction and curation
Extract experimental parameters from methods sections and score accuracy against a labelled set.
- Coding assistants and policy
Use an assistant to write and test analysis code, then review it critically.
- Project: a verified research assistant
Build an LLM tool for a research task and report its measured accuracy and limits.
Software, tools and datasets
Software and tools
All software is free and open source or free for academic use. Recommended computer: 8 GB RAM, 30 GB free disk, Linux, macOS or Windows with WSL2.
Datasets
- Europe PMC open-access subset: Literature retrieval and extraction.Europe PMC
- Labelled abstract sets: Evaluation.Course data
Core reading
- Thirunavukarasu AJ et al. (2023). Large language models in medicine. Nature Medicine 29:1930–1940.
- Lewis P et al. (2020). Retrieval-augmented generation for knowledge-intensive NLP tasks. NeurIPS 33.
Duration, format and fees
- Next cohort
- Dates are sent with your admission offer
- Timetable
- Three evening or weekend sessions a week (IST), plus a lab clinic; intensive and weekend-only schedules on request
- Payment
- Secure online payment link with your admission offer (UPI, cards, net banking). Instalments on request for fees above ₹15,000.
- Refunds
- See the refund and cancellation policy.
Instructor
The instructor for each cohort is named in your admission offer and on this page when the cohort is announced. Vedicgen instructors are selected for hands-on research or industry experience in the subject they teach, and every course is reviewed by a second subject expert.
Questions about who teaches a cohort? [email protected]
Certificate
Learners who complete the course and pass its capstone receive a Vedicgen certificate of completion for Large Language Models for Life Sciences, issued by Vivofold, stating the guided hours and skills assessed. Each certificate has a unique ID and QR code that anyone can check.
Apply for Large Language Models for Life Sciences
Apply
Send the form below. It takes about five minutes.
Review
An adviser confirms your fit and format within two working days.
Offer
You receive dates, timetable and a secure payment link.
Onboard
Set-up guide, pre-reading and joining links.
Frequently asked questions
In the live cohort, modules are taught in instructor-led sessions with a weekly lab clinic where you work on real public data with help at hand; every session is recorded. The self-paced format uses the same lessons, labs and datasets with mentor review of your capstone. Both formats end with an assessed project reviewed for the code and the science.
A laptop with at least 8 GB of RAM and about 30 GB of free disk space, running Linux, macOS, or Windows with WSL2. Installation instructions are sent before the first session, and lab clinics help with any set-up problems.
Yes. Learners who complete the course and pass its assessment receive a Vedicgen certificate of completion, issued by Vivofold. Every certificate carries a unique ID and a QR code that anyone, including an employer or admissions office, can check on the certificate verification page.
Apply with the form on this page. Live and in-person cohorts run through the year; the dates, timetable and joining details of the next cohort are sent with your admission offer and listed on this page once announced. Self-paced enrolment opens as soon as your application is accepted.
Yes. No course runs longer than four weeks, and the length is customisable: choose the standard schedule, an intensive one- to two-week schedule, weekends only, or a custom timetable. Pick your preference on the application form; colleges and teams can book their own dates.
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