Course Format and Delivery

Delivery Method: LiveOnline
Schedule: 3 sessions of 4.5 hour
Cost: $1,350 USD

All sessions are delivered live by an expert instructor in a fully interactive online environment.

*20% off for group bookings when booking 3 or more attendees from the same organization on the same course dates in the same transaction.

About this course

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.

In this course you will learn

• 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).

Topics Covered

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 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.