Prompt Engineering for Singapore Business Teams — Advanced Workshop

Beyond Basic Prompting: What Singapore Business Teams Actually Need

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.

Advanced Prompt Engineering Techniques

Chain-of-Thought Prompting for Business Decisions

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.

Multi-Turn Conversation Architecture

Single-prompt interactions are the lowest level of AI usage. Sophisticated business teams design multi-turn conversation architectures for complex tasks:

1. Context setting — establish the AI's role, the task objective, and constraints
2. Information gathering — feed in relevant data, documents, and background information across multiple messages
3. Analysis — request structured analysis, challenging assumptions and requesting alternative perspectives
4. Refinement — iterate on outputs, requesting specific improvements and adjustments
5. Finalisation — extract the final deliverable in the required format

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.

Few-Shot Learning for Consistent Outputs

When your team needs consistent formatting, tone, or analytical approach across many outputs, few-shot learning is essential:

• Provide 2-3 examples of the desired output before requesting the new output
• Include both good examples and explicitly flagged bad examples to sharpen the AI's understanding
• For recurring tasks (weekly reports, client briefs, data summaries), create standardised few-shot templates that the entire team uses

System Prompts for Role Standardisation

Enterprise AI tools allow system-level prompts that persist across conversations. Use these to establish:

• The AI's role and expertise area
• Output format requirements
• Compliance boundaries (e.g., "Never provide specific legal or financial advice")
• Tone and language requirements (e.g., "Use professional British English. Write for a Singapore business audience")
• Data handling instructions (e.g., "Never include personal names, IC numbers, or contact details in your outputs")

Evaluation and Testing Frameworks

Why Evaluation Matters

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.

Building an Evaluation Framework

Step 1: Define Quality Criteria

For each use case, define what "good" looks like:

• Accuracy — is the output factually correct and logically sound?
• Completeness — does the output address all required aspects of the task?
• Format compliance — does the output match the required structure, length, and style?
• Actionability — can the reader take specific actions based on the output?
• Tone appropriateness — does the output match the intended audience and context?

Step 2: Create Evaluation Rubrics

Score outputs on a 1-5 scale for each quality criterion. Standardised rubrics ensure consistency across evaluators.

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.

Automated Evaluation

For high-volume use cases, build automated evaluation pipelines:

• Use a second AI model to evaluate outputs against your rubric criteria
• Set threshold scores that trigger human review
• Track evaluation metrics over time to detect prompt degradation (e.g., when the underlying AI model is updated)
• Alert the team when outputs fall below acceptable quality thresholds

Enterprise Prompt Standards

The Case for Standardisation

Without prompt standards, every team member develops their own approach. This creates:

• Inconsistent quality — some employees get excellent results while others struggle
• Knowledge silos — effective prompts are trapped in individual employees' heads
• Governance gaps — no visibility into how AI is being used across the organisation
• Onboarding friction — new employees must discover effective prompts through trial and error

Building Your Prompt Standards Library

Structure:

Organise prompts by function and use case:

• Marketing — content creation, campaign analysis, competitive research, social media
• Finance — financial analysis, report generation, forecasting, variance explanation
• HR — job descriptions, policy drafting, training materials, performance review support
• Operations — process documentation, SOP creation, vendor evaluation, project planning
• Legal/Compliance — policy review, regulatory research, contract analysis (with appropriate disclaimers)

For each standard prompt, document:

• Prompt text (the exact wording to use)
• Purpose and expected output
• Required inputs (what context/data the user must provide)
• Quality criteria (how to evaluate whether the output is good enough)
• Version history (when was this prompt last updated and why)
• Owner (who is responsible for maintaining this prompt)

Governance of Prompt Standards

• Quarterly review — review all standard prompts quarterly to ensure they still produce quality outputs (AI model updates can affect prompt effectiveness)
• Feedback loop — establish a channel for employees to report prompts that are not working well or suggest improvements
• New prompt approval — when teams develop new standard prompts, route them through a review process before adding to the library
• Usage tracking — monitor which prompts are being used most and least, and investigate low-adoption prompts

RAG with Internal Documents

What is RAG and Why It Matters

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.

Practical RAG Implementation

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:

• Document ingestion and chunking (breaking large documents into retrievable segments)
• Vector embeddings (converting text into searchable mathematical representations)
• Retrieval (finding the most relevant document chunks for a given query)
• Generation (using the retrieved context to generate accurate, grounded responses)

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.

Best Practices for Singapore Teams

• Start with high-value knowledge — prioritise documents that employees search for frequently: policies, procedures, product specifications, client briefs
• Maintain source accuracy — RAG is only as good as the source documents; implement a content freshness review cycle
• Access controls — ensure RAG systems respect your document-level permissions so that employees only see information they are authorised to access
• PDPA compliance — if internal documents contain personal data, ensure your RAG implementation complies with PDPA requirements for data processing and access

SkillsFuture Subsidised Workshop

Workshop Structure (1 Day)

Morning: Advanced Techniques (3.5 Hours)

• Chain-of-thought, few-shot learning, and multi-turn architecture
• System prompts and role standardisation
• Hands-on exercises with your team's real business tasks
• Enterprise prompt library structure and governance

Afternoon: Evaluation and RAG (3.5 Hours)

• Building evaluation rubrics for your use cases
• Testing and comparing prompt variations with scoring
• RAG concepts and implementation options
• Integration with your existing knowledge management systems
• Prompt standards governance: maintenance, feedback, and improvement cycles

Deliverables

• Customised prompt library for your team's top 10 use cases
• Evaluation rubric templates
• Prompt standards governance document
• RAG implementation assessment for your organisation
• 30-day adoption plan with measurement milestones