Executives, transformation heads, compliance, operations and commercial leads.

Prompt and Context Engineering for Reliable LLMs

• 2 days
• HRDC claimable
• Intermediate

Your team engineers prompts and context for enterprise-grade reliability, reducing hallucinations and keeping outputs accurate and brand-safe, without fine-tuning.

Reliability is the enterprise problem. Even purpose-built legal AI tools using retrieval still produced incorrect information more than 17% of the time in Stanford testing, and general chatbots far more often (Stanford HAI, 2024). Source: Stanford HAI / RegLab, 2024

We build AI inside regulated Malaysian enterprises where a wrong answer is not an option. You get reliability practice grounded in that bar. See a case study.

What your team will be able to do

• Use the CLEAR framework to remove ambiguity from prompts.
• Set contextual guardrails with in-context learning, no fine-tuning needed.
• Reduce hallucinations with few-shot and chain-of-thought techniques.
• Build a central context library for brand tone and standards.
• Set human-in-the-loop validation for AI output.

Who runs it

This program is run by industry practitioners who have shipped AI inside regulated enterprises across banking, government, manufacturing, and telco. It is led by Peter Kua, our AI and data science lead, a core contributor to Malaysia’s National Big Data Analytics Framework under MDEC and co-author of Malaysia’s AI Adoption Blueprints in 2019 and 2020. You are learning from people who have built this in production, not read about it. Peter’s commentary on AI adoption and workforce capability has appeared in The Star, theSun, Singapore Business Review, e27, and DagangNews, and on RTM Berita’s Tech Today.