Prompt and Context Engineering for Reliable LLMs

Program Description

This two-day strategic program is designed for non-technical executives to move beyond "trial and error" AI interactions toward Enterprise-Grade Reliability. In the Malaysian corporate landscape, the primary barrier to AI adoption is "hallucination" and inconsistent outputs.
This workshop focuses on the science of Context Engineering - the art of providing LLMs with the right structural guardrails and proprietary data to ensure every output is accurate, brand-safe, and actionable.
Participants will build no-code workflows that turn AI into a high-fidelity analytical partner, ensuring 100% compliance with PDPA and national AIGE standards.

While this outline serves as a foundational framework with use cases from multiple industries and functions, the final program is fully customized to your industry and internal workflows.

Participants work on real-world problems, not generic examples. We engage in a pre-workshop alignment to inject your specific organizational datasets, pain points, and proprietary use cases directly into the curriculum.

Learning Objectives

Master the Anatomy of a Reliable Prompt: Use the CLEAR framework to eliminate ambiguity and force logical reasoning in AI outputs.
Architect Contextual Guardrails: Learn to provide "In-Context Learning" (ICL) by feeding LLMs specific corporate knowledge without the need for technical fine-tuning.
Mitigate Hallucination & Risk: Implement "Few-Shot Prompting" and "Chain-of-Thought" techniques to ensure the AI "thinks" before it speaks, reducing errors in financial or legal tasks.

Program Details

Content

Day 1: The Architecture of Reliability & Logical Reasoning

Module 1: From "Chatting" to "Engineering" Intelligence

Understanding why LLMs fail and how “Prompt Engineering” acts as the remote control for AI reliability. Exploring the difference between Zero-Shot and Few-Shot learning.
Scenario (Banking): A compliance officer uses a “Zero-Shot” prompt and gets a generic answer; we then re-engineer it using “Few-Shot” examples of past BNM regulatory approvals to get a 100% accurate internal memo.
Hands-on: The “Anatomy Audit” – breaking down a failed prompt and rebuilding it using the Role-Context-Task-Constraint framework.
Expected Impact: Immediate elimination of generic AI “fluff”; foundation for high-precision communication.

Module 2: Chain-of-Thought: Forcing AI Logic

Mastering “Chain-of-Thought” (CoT) prompting to prevent the AI from jumping to wrong conclusions, especially in numerical or logical business cases.
Demo (Manufacturing): Forcing the AI to explain its step-by-step reasoning when calculating “Overall Equipment Effectiveness” (OEE) to ensure the logic matches the factory’s physical reality.
Hands-on: The “Logic Chain” Challenge – participants prompt the AI to solve a complex resource allocation problem, requiring the AI to show its “workings” before giving the final answer.
Expected Impact: 90% reduction in logical errors; increased executive confidence in AI-generated strategy.

Module 3: Contextual Anchoring & In-Context Learning (ICL)

Learning how to “anchor” an LLM to your specific corporate data (PDFs, Tables, SOPs) to ensure it doesn’t “hallucinate” external information.
Scenario (Retail/E-commerce): Feeding the AI a raw product catalog and a localized “Manglish/BM” slang dictionary to generate social media copy that sounds authentically Malaysian.
Hands-on: Build a “Context Buffer” – upload a non-sensitive departmental SOP and engineer a prompt that forces the AI to answer questions only based on that document.
Expected Impact: Ability to turn “General AI” into “Your Company’s AI” without any coding or expensive software.

Module 4: Establishing Technical & Brand Constraints

Using “Negative Prompting” and strict constraints to define what the AI must not do, ensuring brand safety and regulatory compliance.
Scenario: Engineering a prompt for a Customer Service Bot that is strictly forbidden from mentioning competitor names or making specific medical/financial promises.
Hands-on: Create a “Constraint Library” – a set of master prompts that define your corporate “Prohibited Language” and “Mandatory Disclaimers.”
Expected Impact: Structural protection of corporate reputation; 100% alignment with internal legal guidelines.

Day 2: Multi-Step Workflows & Governance

Module 5: Sequential Engineering – The "Agentic" Workflow

Moving from a single prompt to a “Chain of Prompts,” where the output of one task becomes the context for the next, ensuring high-quality complex project delivery.
Scenario (Project Management): Task 1: Analyze Meeting Transcript → Task 2: Extract RAID Log → Task 3: Draft Stakeholder Email.
Hands-on: Build a “Sequential Workflow” – participants engineer a 3-step automation that takes raw data and turns it into a board-ready executive summary.
Expected Impact: 70% reduction in time spent on multi-stage administrative tasks; higher consistency in project outputs.

Module 6: Verification Engineering – The "Critic" Prompt

Using a “Multi-Persona” approach to have one AI prompt critique and verify the work of another, creating a self-correcting internal loop.
Demo (Finance/Legal): Prompt A generates a contract summary; Prompt B (The Auditor) scans it for potential hallucinations or missing clauses.
Hands-on: The “Peer Review” Challenge – participants engineer a “Reviewer Persona” prompt to audit and improve their own Day 1 outputs.
Expected Impact: Significant reduction in “Human-in-the-loop” fatigue; higher accuracy in complex document synthesis.

Module 7: Responsible AI, PDPA & Context Sovereignty

Addressing the “Data Leakage” risk. Understanding what parts of your “Context” are safe for public LLMs and how to anonymize proprietary data.
Scenario (HR): Engineering a prompt for performance review summaries that ensures no NRIC or PII (Personally Identifiable Information) is ever sent to the AI’s “training memory.”
Hands-on: Co-create a “Corporate Prompt Playbook” – outlining do’s/don’ts, data anonymization steps, and “Human-in-the-loop” verification protocols for the team.
Expected Impact: 100% compliance with PDPA 2.0 and national AIGE standards; structural protection of corporate IP.

Module 8: The 90-Day Reliability Roadmap

Consolidating the course into a practical rollout plan for the participant’s specific department.
The Framework: Prioritizing AI-reliability initiatives based on Risk Level vs. Strategic Value.
Hands-on: Develop a “Reliability Backlog” – identifying 3 high-stakes tasks (e.g., weekly variance analysis or legal summaries) to be re-engineered for 100% reliability.
Expected Impact: A clear, actionable path from training to execution; measurable KPIs for “Accuracy Uplift” in the department.

List of Deliverables

Upon completion of the program, participants will have produced a tangible “AI Portfolio” including:

Master Reliability Prompt Library: A repository of Few-Shot, CoT, and Constraint-based prompts for your industry.
Context Engineering Toolkit: A guide on how to structure corporate data for ICL (In-Context Learning).
Custom "Critic" Bot: A personalized AI configuration designed to audit and verify your departmental outputs.

Prerequisites

Technical Knowledge: No prior coding, Python, or technical AI experience is required. This is a non-technical program for business leaders.
Essential Equipment: Participants must bring a laptop with access to web-based tools (ChatGPT, Claude, etc.) and a sample (non-sensitive) business report or dataset.
Mindset: A willingness to move from "Trial-and-Error" to "Intentional Engineering."

Who Should Attend

Training Methodology

Reliability Lab: Hands-on application using actual industry briefs and "broken" prompts for troubleshooting.
Context Engineering Boot Camp: Interactive sessions focusing on structuring data buffers for AI "Anchoring."
Strategic Co-Design: Group sessions to build the corporate AI Playbook and phased 3-6 month adoption roadmap.

100% HRDC-Claimable

This program is fully registered and compliant with HRDC (Human Resource Development Corporation) requirements under the SBL-Khas scheme, allowing Malaysian employers to offset the training costs against their levy.

Certification of Completion

Participants who successfully complete the program will be awarded a “Professional Certificate in Prompt & Context Engineering for Reliable LLMs.“

Post-Workshop Consulting (Optional)

For organizations looking to bridge the gap between training and execution, we offer optional, paid consulting services. These engagements provide expertise and technical support for specific pilot development or full-scale operational integration of the data- and AI-driven use cases established during the program.