Most prompt engineering training teaches the basics — be specific, provide context, use examples. Your team has already figured that out. What Singapore business teams need is advanced prompt engineering that solves real enterprise challenges: consistency across teams, quality assurance at scale, integration with internal knowledge bases, and measurable improvement in output quality.
This workshop is designed for teams that have moved past the experimentation phase and need to operationalise prompt engineering as a core business capability. It assumes familiarity with generative AI tools and focuses on the systems and frameworks that make AI usage reliable, consistent, and measurable across the organisation.
Chain-of-thought prompting forces the AI to show its reasoning, which serves two critical business purposes: it produces better outputs, and it makes those outputs auditable.
Standard prompt: "Analyse whether we should expand into the Vietnam market."
Chain-of-thought prompt: "Analyse whether our company should expand into the Vietnam market. Work through this analysis step by step: (1) Assess the market size and growth trajectory for our product category in Vietnam. (2) Identify the top 3 competitive threats we would face. (3) Evaluate the regulatory requirements for foreign companies in our sector. (4) Estimate the minimum viable investment required. (5) Compare the opportunity cost against expanding deeper in Singapore and Malaysia. (6) Provide your recommendation with a confidence level and the key assumptions underlying it."
The chain-of-thought approach produces analysis that business leaders can evaluate, challenge, and build upon — rather than a conclusion they must either accept or reject blindly.
Single-prompt interactions are the lowest level of AI usage. Sophisticated business teams design multi-turn conversation architectures for complex tasks:
This architecture mirrors how you would work with a human analyst and produces dramatically better results than attempting to get a perfect output from a single prompt.
When your team needs consistent formatting, tone, or analytical approach across many outputs, few-shot learning is essential:
Enterprise AI tools allow system-level prompts that persist across conversations. Use these to establish:
Without evaluation, prompt engineering is guesswork. You might feel that one prompt produces better results than another, but unless you measure systematically, you cannot optimise reliably or demonstrate improvement to leadership.
Step 1: Define Quality Criteria
For each use case, define what "good" looks like:
Step 2: Create Evaluation Rubrics
Score outputs on a 1-5 scale for each quality criterion. Standardised rubrics ensure consistency across evaluators.
| Score | Accuracy | Completeness | Format | Actionability |
|---|---|---|---|---|
| 5 | Fully accurate, no errors | All aspects addressed comprehensively | Perfect format match | Clear, specific actions |
| 4 | Minor inaccuracies, easily corrected | Most aspects addressed | Minor format deviations | Mostly actionable |
| 3 | Some errors requiring verification | Key aspects addressed, gaps in coverage | Acceptable format | Partially actionable |
| 2 | Significant errors | Major gaps | Poor format compliance | Vaguely actionable |
| 1 | Fundamentally inaccurate | Task not addressed | Wrong format | Not actionable |
Step 3: Test and Compare
Run the same task through multiple prompt variations and score each output against your rubric. Track results in a spreadsheet to identify which prompt structures consistently produce the highest-scoring outputs.
Step 4: Iterate and Document
Refine prompts based on evaluation results and document the winning approaches in your team's prompt library.
For high-volume use cases, build automated evaluation pipelines:
Without prompt standards, every team member develops their own approach. This creates:
Structure:
Organise prompts by function and use case:
For each standard prompt, document:
Retrieval-Augmented Generation (RAG) combines the generative capabilities of AI models with your organisation's internal knowledge. Instead of the AI answering from its general training data, RAG retrieves relevant documents from your knowledge base and uses them to generate accurate, contextually specific responses.
For Singapore business teams, RAG transforms AI from a generic writing assistant into a tool that knows your company's policies, procedures, products, clients, and institutional knowledge.
Option 1: Microsoft Copilot with SharePoint If your organisation uses Microsoft 365, Copilot already provides RAG capabilities by searching SharePoint, OneDrive, and Teams content. The key is ensuring your content is well-organised, properly tagged, and permissioned correctly.
Option 2: Custom RAG with Enterprise AI Tools For more sophisticated requirements, build custom RAG pipelines using:
Option 3: AI Platforms with Built-In RAG Several enterprise AI platforms offer built-in RAG functionality: ChatGPT Enterprise with file upload, Claude with project knowledge bases, and specialised platforms like Glean and Guru.
Morning: Advanced Techniques (3.5 Hours)
Afternoon: Evaluation and RAG (3.5 Hours)
Basic prompt engineering teaches how to write clear, specific prompts that produce useful AI outputs. Advanced prompt engineering focuses on enterprise-scale challenges: chain-of-thought reasoning for auditable analysis, few-shot learning for consistent outputs, evaluation frameworks for quality assurance, standardised prompt libraries for team-wide consistency, and RAG implementation for grounding AI responses in your organisation's knowledge. The jump from basic to advanced is the difference between individual productivity and organisational capability.
Measure three things: output quality (using evaluation rubrics to score AI outputs before and after training), adoption rates (percentage of team members regularly using AI tools and standard prompts), and time savings (tracked through self-reporting or workflow timestamps). We provide baseline measurement templates and a 30-day tracking framework as part of the workshop deliverables.
Yes. This workshop is designed for business professionals, not engineers. The advanced techniques covered (chain-of-thought prompting, few-shot learning, evaluation frameworks, prompt standards) are all applicable to business tasks like report writing, analysis, client communications, and strategic planning. No coding or technical background is required. RAG implementation is covered at the conceptual and decision-making level, with technical deep-dives available as an optional add-on.
The workshop itself qualifies for SkillsFuture Enterprise Credit (up to S$10,000 per employer covering 90% of out-of-pocket costs) and SkillsFuture Mid-Career Enhanced Subsidy for employees aged 40 and above. We assist with the funding application process as part of our engagement. The PSG grant may also apply for qualifying SMEs adopting AI productivity solutions.
