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Bio3· Biotech and life sciences

AI Research Workflows for Life Sciences

You will run evidence reviews and curate research datasets with AI doing the heavy lifting, and you will be able to prove, with an audit trail, that the AI did not miss or invent anything that matters.

Choose a planRead the syllabus
Who it is for
MSc students in biotechnology, microbiology, bioinformatics, cancer biology, food science and biomedical science who write reviews, theses and papers, or who will work on research and data teams.
AI core
9 units · 20 h
This course
6 modules · 12 lessons · 7.3 h
Live projects
2, on Professional and Career
01Roles it prepares you for
  • Data Curator
  • Research Associate
  • Research Assistant (evidence synthesis)

The skills these roles ask for. Preparation, not a promise of a job.

02Tools you will use
  • ASReviewFree tierVerified
  • NotebookLMFree tier
  • Deep research mode in an AI assistantFree tier
  • PubMedFree tier
  • Europe PMCFree tier
  • ZoteroFree tier
  • RayyanFree tier
  • Google SheetsFree tier
  • ClaudeFree tier
  • ChatGPTFree tier

You never pay for a tool subscription. “Verified” means we checked the free access on the vendor’s own site this month. Tool names describe what you learn; no vendor endorses Launchpad.

03Syllabus

What you will learn, in order.

Lesson films are narrated with an AI voice, over visuals made with AI tools and checked by our team. Saarthi, the coach, is an AI. Mentors and reviewers are people.

Part 1 · The AI core, shared by every stream

00Set up your coach1 hLearn how the core works, write the learning-context document that tells every AI tool who you are, and set Saarthi up as a coach that makes you think instead of thinking for you.
  1. How this core works6 min
  2. Write your learning-context document8 min
  3. Seven roles for AI, and study modes7 min
01How LLMs work and fail2.5 hBuild a working mental model of a large language model: tokens, prediction, context windows and memory. Then learn its predictable failures (hallucination, sycophancy, stale knowledge, bad arithmetic) and reproduce them yourself.
  1. Tokens and next-word prediction7 min
  2. Context windows and memory7 min
  3. Hallucination, sycophancy and other failures8 min
  4. Lab: one prompt, three models, five failures8 min
02Description and context engineering3.5 hWrite briefs that get the right output the first time: role, task, context, examples and format. Then make them reusable with Claude and ChatGPT Projects, Gemini Gems, custom GPTs and NotebookLM, and learn to work with PDFs, spreadsheets and images.
  1. The brief: role, task, context, format8 min
  2. Examples and output formats7 min
  3. Projects in Claude and ChatGPT8 min
  4. Gems, custom GPTs and NotebookLM8 min
  5. Working with files: PDFs, spreadsheets, images8 min
03Verify everything2.5 hCheck AI output the way fact-checkers do: lateral reading, tracing citations and numbers to the original, using deep-research modes without trusting them blindly, and testing a prompt properly with a 10-case golden set.
  1. Lateral reading7 min
  2. Checking citations and numbers8 min
  3. Deep-research modes: use and limits7 min
  4. Your first eval: a 10-case golden set8 min
04Connectors and MCP2 hConnect AI to the places your work lives (Drive, Gmail, Sheets, business data) without writing code. Learn what an MCP server is, why a reviewed directory differs from unreviewed community servers, how to grant the least access that works, and how to keep browser extensions on a short leash.
  1. What connectors and MCP are8 min
  2. Trust, least privilege and the reviewed directory8 min
  3. Browser extensions and AI browsers7 min
  4. Lab: ask the seller-data MCP server, check every answer8 min
05Agents and automations3.5 hHand multi-step work to AI safely. Tell an agent from an automation, decide what to delegate, use browser and coding agents with care, and build an n8n workflow that uses AI with a human-approval step before anything leaves the system.
  1. Agents vs automations: what to delegate8 min
  2. Browser agents, safely7 min
  3. Automation basics with n8n and Zapier8 min
  4. Lab: build an automation with a human-approval step8 min
  5. Failure modes and your failure-mode note7 min
06AI for learning and careers1.5 hUse AI to learn faster without outsourcing the learning, write applications that still sound like you, and practise interviews out loud with Saarthi.
  1. Learning with AI, not instead of learning7 min
  2. Applications in your own voice7 min
  3. Mock interview with Saarthi8 min
07Diligence: law, ethics, integrity2.5 hThe rules that apply when you use AI in India: the DPDP Act 2023 and its 2025 Rules, academic integrity and disclosure, copyright (including where Indian law is still unsettled), and bias. You finish by writing your own AI-use policy.
  1. DPDP Act 2023: personal data and AI8 min
  2. Academic integrity and disclosure7 min
  3. Copyright and AI in India8 min
  4. Bias and fairness7 min
  5. Write your personal AI-use policy7 min
08Core capstone1 hPut all four Ds together on one anonymised marketplace settlement file: ask good questions, automate one step, verify every number against Saarthi's P&L engine, and defend your work live in five minutes.
  1. The capstone brief6 min
  2. Lab: ask, automate, verify8 min
  3. Prepare your 5-minute defence7 min

Part 2 · AI Research Workflows for Life Sciences

01Framing questions and writing a protocol2 lessonsTurning a vague topic into an answerable question and a protocol that fixes your methods before you see results.
  1. From topic to answerable question25 min
  2. Lab: write a review protocol with an AI sparring partner40 min
02Searching that others can reproduce2 lessonsBuilding Boolean and MeSH search strings, logging searches, and de-duplicating records from more than one database.
  1. Boolean, MeSH and the search log25 min
  2. Lab: build, test and log a two-database search45 min
03AI-assisted screening and its audit2 lessonsHow active-learning and LLM screening work, how to measure what they miss, and how to report it honestly in PRISMA.
  1. Active learning, LLM screening and recall30 min
  2. Lab: screen with ASReview and audit it50 min
04Extraction, synthesis and grounded AI tools2 lessonsExtracting data with AI under verification, using source-grounded notebooks and deep research tools, and catching fabricated citations.
  1. Grounded tools, deep research and fabricated citations30 min
  2. Lab: verified extraction from included studies45 min
05Curating datasets people can reuse2 lessonsFAIR principles, metadata, units, missing data, licences and versioning, with AI used to draft and check documentation.
  1. FAIR data and the anatomy of a good dataset30 min
  2. Lab: curate a messy lab dataset50 min
06Disclosure, integrity and the review conversation2 lessonsDisclosing AI use in theses and papers, protecting confidential data, and defending your methods to a reviewer.
  1. Disclosing AI use and protecting research integrity25 min
  2. Lab: methods section, AI statement and mock review45 min
04Live projects

Two briefs. Real-shaped work.

  1. Project 1

    Systematic-review protocol with an AI screening audit

    Choose a life-sciences question from the provided list and produce a complete, versioned systematic-review protocol plus a run of title and abstract screening with a full AI audit. Frame the question with an appropriate framework, pilot your eligibility rules with a second reviewer, build and test a search in at least two databases against known relevant papers, de-duplicate, and screen with an active-learning tool under a stopping rule written before you start. Audit the AI with an independent dual-human random sample of unscreened records, estimate recall, compare an LLM screener on a subset, and explain every missed record. Report everything in PRISMA 2020 style with an AI use statement.

    You hand in

    • Versioned protocol with eligibility pilot results and deviations log
    • Search log for two or more databases, with sensitivity test against known papers
    • De-duplication counts and a PRISMA 2020 flow diagram up to title/abstract screening
    • Active-learning screening log with the pre-set stopping rule applied
    • Dual-human audit sample results with estimated recall and an analysis of misses
    • LLM screening comparison with recall and precision
    • Methods section and AI use statement

    Data: Public PubMed and Europe PMC records retrieved by the student's logged search (typically 300–600 after de-duplication), plus a mentor-supplied list of real PMIDs of known relevant papers for the sensitivity test. No patient-level data.. Reviewed by Saarthi against a published rubric, signed off by a mentor.

  2. Project 2

    Dataset curation and provenance report

    Take a public life-sciences dataset (from a repository such as GEO, ENA or Zenodo, chosen from the provided shortlist) or the Saarthi-provided synthetic multi-file lab dataset, and turn it into a FAIR-ready package that a stranger could reuse correctly. Keep raw data untouched, clean with every step recorded, build a complete data dictionary with units and missing codes, choose and justify a licence, check for personal data and quasi-identifiers, and write a provenance report that traces every value in the clean files back to raw. Use AI to profile, draft documentation and write cleaning code, and record every AI guess you corrected. Prove reusability with a stranger test.

    You hand in

    • Package with data/raw (untouched) and data/clean folders
    • Data dictionary with description, type, unit, allowed values and missing code for every variable
    • Provenance report tracing raw to clean, with tools and reasons for each step
    • README with citation, licence choice and reasoning, and known limitations
    • Personal-data and quasi-identifier check with actions taken
    • Log of AI suggestions accepted, corrected or rejected
    • Stranger-test questions and results

    Data: Either a public dataset from a Saarthi shortlist of open repositories (with its original licence respected) or a Saarthi-provided synthetic microbiology dataset with deliberate inconsistencies. No real personal data.. Reviewed by Saarthi against a published rubric, signed off by a mentor.

05Practise out loud

In voice mock interviews and client calls, Saarthi plays: Dr. Shetty, a research coordinator at a university research centre who reviews student protocols and data deposits. She has seen AI-screened reviews miss key trials and datasets with no units or licence. She asks how you know your screening found everything, and whether a stranger could reuse your data without emailing you.

On plans that include mock interviews. You get written feedback after every call.

06Enrol

Pick a plan for AI Research Workflows for Life Sciences.

One payment. The access deadline for each plan is shown before you pay.

  • Foundation

    01

    Learn the AI your field now runs on.

    ₹2,999

    One payment. GST included.

    • Access for 120 days (about 4 months) from the day you pay
    • No 1:1 mentor sessions
    • The common AI core and your domain course
    • Saarthi, your AI coach, any hour
    • Hands-on labs on free tools
    • Verifiable certificate
    Enrol on Foundation

  • Professional

    02

    Prove it on real work.

    ₹7,999

    One payment. GST included.

    • Access for 180 days (about 6 months) from the day you pay
    • One 1:1 session with a mentor
    • Everything in Foundation
    • Two live projects, reviewed by Saarthi and a mentor
    • Public portfolio page of verified work
    • Voice mock interviews
    • One 1:1 coaching session
    Enrol on Professional

  • Career

    03

    Work alongside our team.

    ₹14,999

    One payment. GST included.

    • Access for 240 days (about 8 months) from the day you pay
    • Four 1:1 sessions with a mentor
    • One 1:1 call with an industry expert
    • Everything in Professional
    • Four-week supervised practicum with the Saarthi team
    • Four 1:1 coaching sessions with your mentor
    • An exclusive 1:1 call with an industry expert in your field
    • CV and LinkedIn review
    • Priority mentor replies
    Enrol on Career

Your access period starts the day you pay and is shown before you pay. One exam pause per enrolment, for up to 15 days. When access ends the course locks, and your portfolio page and certificate stay live. Full refund within 7 days, pro-rata after that, and an automatic refund for any practicum, 1:1 mentor or expert session we do not deliver. Refund policy