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.

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

• 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).