Generative AI Bootcamp – Pharma

An immersive bootcamp for software, QA, DevOps, and data teams to adopt Generative AI in regulated biopharma, covering fundamentals, AI-assisted development, and compliant SDLC with GxP, Part 11, privacy safeguards.

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There are currently no scheduled classes for this course.

Overview

This immersive bootcamp equips software and platform engineers, QA/validation, DevOps, and 

data teams with the knowledge and guardrails to use Generative AI safely and effectively in a 

regulated biopharma environment. Participants learn GenAI basics (LLMs, transformers, 

prompting), apply AI-assisted development with Copilot and “vibe coding” collaboration 

patterns, and operationalize GenAI across the SDLC while meeting GxP, Part 11, data privacy, 

and IP requirements

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Full course details

Course outline

Module 1: GenAI 101 for Biopharma 

• LLMs/transformers, context windows, grounding/RAG, hallucinations.

• High-value biopharma use cases: protocol parsing, validation docs, SOP helpers, QMS 

tooling, CSV test scaffolds, PV triage, MFG exception analysis.

• Risks: data leakage, IP, bias, safety.

Hands-on Lab: Identify 5 internal use cases; classify by value/risk; map to guardrails.

Module 2: Compliance & Guardrails 

• GxP/Part 11, data residency, access control, audit trails, model risk tiers.

• “Allow/deny” patterns, red-team prompts, record retention & attribution.

Workshop: Draft a 1-page team “AI Use Policy” + prompt safety checklist.

Module 3: Prompt Engineering & Vibe Coding 

• Task decomposition, role prompting, chain-of-thought proxies, critique loops.

• Vibe coding patterns: co-creation sessions, driver/navigator with AI, guardrail breaks, 

acceptance criteria alignment.

Lab: Turn a user story into design notes, stubs, and tests via vibe coding.

Module 4: GitHub Copilot Essentials 

• Copilot Chat, inline completions, test generation, code refactors, doc blocks.

• Repo policy, telemetry settings, secret hygiene, license/IP considerations.

Lab: Demo Copilot in a sandbox repo; generate a service + unit tests; log what was AI-generated 

for audit.

Module 5: AI in the SDLC 

• Requirements → acceptance criteria → code → tests → docs → reviews.

• Traceability with issues/PRs; storing prompts/outputs as validation artifacts.

Lab: How can you use AI to aid value delivery in your lifecycle?

Module 6: Testing, QA & Validation 

• AI for unit/integration tests, boundary & property tests, mutation testing.

• CSV/CSA alignment: objective evidence, independence, change control.

Lab: Generate test suites with Copilot, run, capture evidence in pipeline.

Module 7: DevOps, CI/CD & Observability

• AI-assisted pipelines (lint, SAST/DAST, SBOM), policy-as-code, gated deploys.

• ChatOps for PR review and post-deploy checks.

Lab: Add AI-generated pipeline steps; enforce policy gates; store build artifacts for audit.

Module 8: RAG, Data Safety & Domain Grounding 

• Safe retrieval (vector stores, ACLs), PHI/PII handling, prompt shielding.

• When to prefer patterns over free-form generation (templates, controlled gen).

Lab: Demo a RAG helper showcasing how agents can support delivery

Module 9: Adoption Plan, Metrics & Next Steps 

• Roles & responsibilities, ambassador model, training paths, sandbox → pilot → scale.

• KPIs: lead time, escaped defects, validation effort saved, rework, security findings.

Audience / prerequisites

This course is best suited for professionals in roles such as:

  • Project Managers / Scrum Masters
  • Business Analysts
  • Product Owners / Product Managers
  • Developers / Engineers
  • IT or Operations professionals
In this class you will learn how to

• Explain how LLMs work, their limits, and where GenAI adds value in pharma R&D, 

manufacturing, quality, PV, and IT.

• Use prompt engineering patterns, vibe coding, and GitHub Copilot to accelerate 

design, coding, test, and documentation—with guardrails.

• Integrate AI into SDLC/DevOps (requirements → code → tests → docs → review) 

with traceability, validation, and auditability.

• Apply compliance controls: data protection (PII/PHI), model/use-policy, 

allowed/blocked prompts, change control, and validation evidence.

• Measure impact (quality, speed, risk) and define a rollout plan (personas, training, 

governance, KPIs).

Generative AI Bootcamp - Pharma Schedule

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There are currently no scheduled classes for this course.

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