ChatGPT Prompt Engineering

Advanced AI Prompt for Finance (Cost Optimization)

Advanced AI Prompt for Finance (Cost Optimization)

You are a financial analyst specializing in cost optimization.

Review the following expense data and identify:

1. Top categories with unnecessary costs.

2. Opportunities to reduce overhead without affecting productivity.

3. Forecast of potential savings if recommendations are applied.

Data: [

📊 Example Expense Data :

1. Top Categories with Unnecessary Costs

Expense Category	Annual Spend (USD)	Comment / Pain Point
Business Travel & Hotels	$420,000	Frequent non-essential trips, premium hotels
Office Supplies	$95,000	Excess paper, printing, and unused materials
Software Licenses	$180,000	Overlapping tools with similar functions
Marketing Events	$250,000	Low ROI trade shows and sponsorships

2. Opportunities to Reduce Overhead (Without Affecting Productivity)

Category	Recommended Action	Estimated Reduction
Business Travel & Hotels	Shift to virtual meetings, standardized hotel policy	-30% ($126,000)
Office Supplies	Digitize processes, limit physical printing	-40% ($38,000)
Software Licenses	Consolidate overlapping tools, negotiate contracts	-25% ($45,000)
Marketing Events	Focus on high-ROI digital campaigns	-35% ($87,500)

3. Forecast of Potential Savings

Category	Current Spend	Potential Savings	Forecasted Spend After Optimization
Business Travel & Hotels	$420,000	$126,000	$294,000
Office Supplies	$95,000	$38,000	$57,000
Software Licenses	$180,000	$45,000	$135,000
Marketing Events	$250,000	$87,500	$162,500
Total	$945,000	$296,500	$648,500

✅ Executive Insight:
By applying these targeted efficiency measures, the company could reduce overhead by ~31% annually (~$296K savings), without impacting core productivity. The largest gains come from travel rationalization and marketing optimization, while digital transformation (supplies + software) ensures sustainable long-term efficiency.

]

Output format:

- Executive Summary

- Cost Categories & Inefficiencies

- Savings Opportunities

- Forecasted Impact

____________________________________________________________________________

ChatGPT Answer:

Here’s the structured financial analysis framed as a cost optimization review:

Executive Summary

The company currently spends $945,000 annually across key operational categories, with approximately $296,500 in identifiable inefficiencies. By implementing targeted measures—particularly in travel, marketing, and digital process optimization—the company could achieve a 31% reduction in overhead costs without compromising productivity.

Cost Categories & Inefficiencies

Business Travel & Hotels – $420,000

Pain Point: Frequent non-essential trips and use of premium hotels.

Office Supplies – $95,000

Pain Point: Excessive paper usage, printing, and unused materials.

Software Licenses – $180,000

Pain Point: Overlapping tools and redundant licensing costs.

Marketing Events – $250,000

Pain Point: Low ROI trade shows and sponsorship activities.

Savings Opportunities

Business Travel & Hotels: Shift to virtual meetings and standardized travel policy → 30% savings ($126,000).
Office Supplies: Digitize workflows, enforce paperless practices → 40% savings ($38,000).
Software Licenses: Consolidate overlapping tools, renegotiate vendor contracts → 25% savings ($45,000).
Marketing Events: Focus budget on digital campaigns with measurable ROI → 35% savings ($87,500).

Forecasted Impact

Category	Current Spend	Potential Savings	Optimized Spend
Business Travel & Hotels	$420,000	$126,000	$294,000
Office Supplies	$95,000	$38,000	$57,000
Software Licenses	$180,000	$45,000	$135,000
Marketing Events	$250,000	$87,500	$162,500
Total	$945,000	$296,500	$648,500

✅ Overall Impact:
Through these optimizations, the company stands to save ~$296K annually, creating both immediate financial relief and long-term operational efficiency.

-Wiwin Wijaya, 2025-

By modifying it to suit your (any) context or obstacles, your company will save approximately $30,000 in third-party consulting costs 😉

“With the right AI prompts, your business can become more efficient and creative.”

Prompt for Marketing (Market Analysis)

Prompt for Marketing (Market Analysis)

You are a senior marketing strategist for a global consultancy.
Analyze the following customer survey data to identify:
1. Top 3 customer pain points.
2. Key opportunities for brand positioning.
3. Suggested messaging angles that could resonate with the target audience.

Data: [paste survey data here]

Output format:
- Executive Summary (2-3 sentences)
- Customer Pain Points
- Brand Opportunities
- Recommended Messaging

---------------------example data:-----------------------

Data: Consumer Preference Survey for E-commerce Services in 2025

No. Demographics (Age) Gender Frequency of Online Shopping Favorite Product Category Main Reason for Online Shopping Most Frequently Used E-commerce Platform Satisfaction Level (1–5)

1 18–24 Male 2–3 times/week Fashion Promo prices & discounts Shopee 4

2 25–34 Female 1 time/week Beauty Convenience & practicality Tokopedia 5

3 35–44 Male 2 times/month Electronics Product variety Lazada 3

4 18–24 Female 3–4 times/week Fashion & Accessories Latest trends & styles TikTok Shop 4

5 45–54 Male 1 time/month Household appliances Save time Tokopedia 3

6 25–34 Female 2 times/week Food & Beverages Shipping promotions Shopee 5

7 35–44 Female 1–2 times/week Kids' products Practical & safe Tokopedia 4

8 18–24 Male 4–5 times/week Gadgets & Accessories Cheaper prices on TikTok Shop 4

9 25–34 Women 3 times/month Health & Supplements Influencer recommendations on Shopee 4

10 35–44 Men 1 time/week Electronics Official Lazada guarantee 5

Pre-analysis

- Ages 18–24 shop online most frequently.

- Hopee & Kokopedia are still dominant, but TikTok Shop is starting to attract a younger segment.

- Favorite products shift by age: fashion for young people, electronics & household items for adults.

- Average satisfaction level is quite high (3–5).

ChatGpt answer:

Here’s the structured analysis based on the provided survey data, framed as a senior marketing strategist would present it:

Executive Summary

Young consumers (18–24) are the heaviest online shoppers, with strong preference for fashion and gadgets, while older demographics lean toward electronics and household products. Overall satisfaction is moderate-to-high (3–5), but gaps remain in product variety, time efficiency, and platform reliability. These insights highlight opportunities to strengthen brand positioning through trust, convenience, and tailored value propositions.

Customer Pain Points (Top 3)

Product Variety & Availability – Mid-age consumers (35–44) show lower satisfaction (3/5) when shopping electronics, indicating unmet expectations in assortment or product relevance.
Efficiency & Time-Saving – Older segments (45–54) cite “time-saving” as their primary reason but remain only moderately satisfied (3/5), suggesting that ease of navigation, delivery speed, or user experience could be improved.
Price Sensitivity vs. Value Assurance – Younger buyers are highly motivated by discounts, promos, and “lower prices,” but satisfaction doesn’t peak at 5, signaling potential concerns over quality or trustworthiness despite price appeal.

Brand Opportunities

Position as a “Smart Shopper’s Platform” for younger demographics by combining affordability with credibility (discounts + verified product quality).
Enhance trust and assurance for mid-to-older consumers through guarantees, warranties, and reliable post-purchase support.
Differentiate on convenience by streamlining shopping journeys (fast delivery, one-click checkout, curated recommendations) to address time-saving expectations.

Recommended Messaging Angles

For Gen Z (18–24):
- “Trendy styles, verified quality, unbeatable deals.”
- Emphasize affordability + trend relevance while reinforcing authenticity.
For Millennials (25–34):
- “Smart choices made simple—shop fast, shop safe.”
- Highlight convenience, promotions, and curated shopping experiences.
For Older Segments (35+):
- “Trusted brands, official warranties, delivered to your door.”
- Focus on reliability, guarantees, and hassle-free time savings.

Would you like me to also map these insights into a positioning matrix (Value vs. Trust) so it’s visually clear where each platform (*hopee, *okopedia, *azada, *ikTok Shop) stands in the consumer’s mind?

Here’s the E-commerce Brand Positioning Matrix (Value vs. Trust) based on the survey insights:

*hopee → Strongest in value/price appeal with solid trust.
*okopedia → Strongest in trust/reliability, balanced value.
*azada → Middle ground but weaker on both axes, indicating need for clearer differentiation.
*ikTok Shop → High price appeal for Gen Z, but relatively lower trust perception.

Do you want me to also suggest strategic moves for each platform to shift their positioning upwards/rightwards in this matrix?

here’s a strategic roadmap for each platform to strengthen its positioning in the Value vs. Trust matrix:

**1. *hopee (High Value, Moderate–High Trust)**

Current: Perceived as affordable with lots of promos, but trust isn’t absolute (quality/authenticity concerns).
Strategic Move:

Enhance product verification (e.g., “Shopee Verified” badges).
Invest in brand partnerships for exclusive collections.
Expand buyer protection programs.
Goal: Shift from “cheap deals” to “smart, safe shopping with real value.”

**2. *okopedia (Balanced Value, Highest Trust)**

Current: Trusted by consumers, especially older segments; seen as reliable and convenient.
Strategic Move:

Boost value perception by introducing more aggressive promotions.
Develop personalized recommendation engines to increase perceived convenience.
Position as the “everyday reliable choice.”
Goal: Become the premium-trust yet affordable platform — “the safe default.”

**3. *azada (Mid-Level Value & Trust)**

Current: Middle ground; decent for electronics but doesn’t stand out. Satisfaction is mixed (3–5).
Strategic Move:

Differentiate on exclusive electronic brands and official warranties.
Improve delivery speed and after-sales support.
Position as the specialist platform for electronics & home tech.
Goal: Move upward in trust by emphasizing authenticity + warranty guarantee.

**4. *ikTok Shop (High Value, Lower Trust)**

Current: Strong appeal to Gen Z via low prices and trends, but perceived as risky (product quality/legitimacy).
Strategic Move:

Launch influencer-backed “Trust & Trend” campaigns (curated products by trusted creators).
Introduce a “TikTok Certified Store” label for verified sellers.
Highlight speed and trendiness while slowly building credibility.
Goal: Retain Gen Z price appeal but climb upward by reinforcing quality & authenticity.

👉 In short:

*hopee: From “deals” → “smart + safe deals.”
*okopedia: From “trustworthy” → “trustworthy + value leader.”
*azada: From “neutral” → “electronics specialist.”
*ikTok Shop: From “cheap trends” → “trusted trends.”

-Wiwin Wijaya, 2025-

By modifying it to suit your (any) context or obstacles, your company will save approximately $30,000 in third-party consulting costs 😉

“With the right AI prompts, your business can become more efficient and creative.”

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General FAQ - Prompt Engineering

1. Why is Prompt Engineering Important in AI?
Prompt engineering is important because the quality of the prompt determines how the AI model responds. With the right prompt, we can guide the AI to be more relevant, accurate, and contextually appropriate. This greatly helps in improving efficiency, reducing misunderstandings, and achieving the desired output.
1. Understanding Basic Prompting Techniques in the Context of AI.
Basic prompting techniques include:
- Direct instructions: Providing explicit commands.
- Few-shot prompting: Giving examples for the model to follow a pattern.
- Contextual questions: Providing context for more relevant responses.
- Role prompting: Directing the AI to adopt a specific role.
1. Using Prompting to Improve Language Model Performance. Prompting can enhance performance by clarifying instructions, limiting the scope of responses, and providing structure. For example, instead of asking, “Tell me about Earth,” you could write, “Explain Earth in 3 concise points about its size, atmosphere, and role in supporting life.
1. Crafting Effective Prompts: Tips and Strategies.
- Be clear and specific.
- Use formats (lists, bullet points, tables).
- Include examples when necessary.
- Limit responses with word count or structure instructions. These strategies help reduce vague or off-topic responses.
1. Applying Prompt Engineering to Chatbots and Virtual Assistants. In chatbots, prompts can be used to:
- Set the tone of communication (formal, casual, etc.).
- Limit the domain of responses.
- Handle ambiguous questions with clarification.
- Provide system instructions to ensure consistent chatbot “personality.”
1. Use Cases of Prompt Engineering in Information Retrieval. In search applications, prompts can direct the AI to:
- Filter relevant information.
- Provide concise answers from lengthy sources.
- Add context (e.g., “Explain in simple language for high school students”).
1. Mitigating Risks and Challenges in Prompt Engineering. Risks include bias, misleading responses, or inconsistent outputs. To mitigate:
- Conduct iterative testing.
- Use neutral prompts.
- Include instructions for fact verification.
1. Case Study Examples: Successes of Prompt Engineering in Industry. Examples:
- Customer support: Prompts enable chatbots to respond according to standard operating procedures (SOPs).
- Legal tech: Prompts help AI summarize legal documents with a specific structure.
- E-commerce: Prompts are used to create persuasive product descriptions.
1. Combining Prompt Engineering with Other NLP Techniques. Prompts can be combined with:
- Fine-tuning: Retraining models for specific tasks.
- RAG (Retrieval-Augmented Generation): Integrating external data with prompts.
- Chain-of-thought: Guiding the model to think step-by-step.
1. The Role of Prompt Engineering in Improving AI Responsiveness. Prompts make AI more responsive by:
- Providing explicit instructions.
- Structuring conversation flow.
- Reducing the model’s need to “guess” the user’s intent.
1. The Importance of Evaluating and Refining Prompts for Better Results. Prompts are not always perfect initially. Evaluation involves testing various prompt variations and refining based on results. This iteration is crucial for consistently high-quality outputs.
1. Building an Efficient Prompt Engineering Framework. The framework includes:
1. Defining objectives.
2. Designing initial prompts.
3. Testing and evaluation.
4. Documenting results.
5. Continuous optimization.
1. Addressing Bias in Prompt Engineering: A Fair Approach. To reduce bias:
- Use neutral language.
- Avoid stereotypical assumptions.
- Test with diverse user groups (age, culture, gender).
- Include explicit instructions for balanced responses.
1. Ethical and Security Challenges in Using Prompt Engineering. Main challenges:
- Misuse (e.g., manipulating information).
- Data privacy.
- Potential discrimination. Solutions: Establish usage guidelines, audit outputs, and ensure transparency.
1. The Future of Prompt Engineering: Expected Trends and Innovations. Future trends:
- Automated prompt optimization: AI that crafts its own prompts.
- Integration with multimodal AI: Combining text, images, and audio.
- Standardized frameworks for prompt evaluation.
- Collaboration with AI that understands deeper contexts.

------------------------------------------------------------------Bahasa Indonesia------------------------------------------------------

1) Mengapa Prompt Engineering itu penting dalam AI?
Prompt engineering penting karena kualitas prompt menentukan bagaimana model AI merespons. Dengan prompt yang tepat, kita bisa mengarahkan AI agar lebih relevan, akurat, dan sesuai konteks. Ini sangat membantu untuk meningkatkan efisiensi, mengurangi kesalahpahaman, dan mendapatkan output yang diinginkan.

2) Memahami Teknik Dasar Prompting dalam Konteks AI.
Teknik dasar prompting meliputi:

Instruksi langsung: Memberikan perintah eksplisit.
Contoh (few-shot prompting): Memberikan contoh agar model meniru pola.
Pertanyaan kontekstual: Menyediakan konteks agar jawaban lebih relevan.
Role prompting: Mengarahkan AI untuk “berperan” tertentu.

3) Menggunakan Prompting untuk Meningkatkan Kinerja Model Bahasa.
Prompting dapat meningkatkan kinerja dengan memperjelas instruksi, membatasi ruang jawaban, dan memberi struktur. Misalnya, alih-alih bertanya “Ceritakan tentang bumi”, kita bisa menulis “Jelaskan bumi dalam 3 poin singkat mengenai ukuran, atmosfer, dan perannya bagi kehidupan.”

4) Menyusun Prompt yang Efektif: Tips dan Strategi.
Tips:

Jelas dan spesifik.
Gunakan format (daftar, poin, tabel).
Tambahkan contoh bila perlu.
Batasi jawaban dengan instruksi jumlah kata/struktur.
Strategi ini membantu mengurangi jawaban melantur.

5) Menerapkan Prompt Engineering pada Chatbot dan Asisten Virtual.
Pada chatbot, prompt dapat digunakan untuk:

Mengatur nada komunikasi (formal, santai, dll).
Membatasi domain jawaban.
Menangani pertanyaan ambigu dengan klarifikasi.
Memberi instruksi sistem agar chatbot konsisten dalam “kepribadian”-nya.

6) Kasus Penggunaan Prompt Engineering dalam Pencarian Informasi.
Dalam pencarian, prompt bisa diarahkan agar AI:

Menyaring informasi relevan.
Memberi jawaban ringkas dari sumber panjang.
Menambahkan konteks (misal: “Jelaskan dalam bahasa sederhana untuk pelajar SMA”).

7) Memitigasi Risiko dan Tantangan dalam Prompt Engineering.
Risikonya antara lain: bias, jawaban menyesatkan, atau hasil tidak konsisten. Untuk memitigasi:

Uji coba berulang.
Gunakan prompt netral.
Tambahkan instruksi untuk verifikasi fakta.

8) Contoh Kasus Studi: Keberhasilan Prompt Engineering dalam Industri.
Contoh:

Customer support: Prompt membuat chatbot bisa menjawab sesuai SOP.
Legal tech: Prompt membantu AI merangkum dokumen hukum dengan struktur tertentu.
E-commerce: Prompt dipakai untuk menyusun deskripsi produk yang persuasif.

9) Menggabungkan Prompt Engineering dengan Teknik NLP Lainnya.
Prompt bisa dikombinasikan dengan:

Fine-tuning: melatih ulang model agar lebih khusus.
RAG (Retrieval-Augmented Generation): menggabungkan data eksternal dengan prompt.
Chain-of-thought: memandu model berpikir langkah demi langkah.

10) Peran Rekayasa Prompt dalam Meningkatkan Responsivitas AI.
Prompt membantu AI lebih responsif dengan:

Memberi instruksi eksplisit.
Menyusun alur percakapan yang terarah.
Mengurangi waktu model “menebak” maksud pengguna.

11) Pentingnya Evaluasi dan Penyesuaian Prompt untuk Hasil yang Lebih Baik.
Prompt tidak selalu sempurna di awal. Evaluasi dilakukan dengan uji coba terhadap berbagai variasi prompt, lalu menyesuaikan berdasarkan hasil. Iterasi ini penting agar output semakin konsisten dan berkualitas.

12) Membangun Kerangka Kerja Prompt Engineering yang Efisien.
Kerangka kerja meliputi:

Menentukan tujuan.
Mendesain prompt awal.
Uji coba & evaluasi.
Dokumentasi hasil.
Optimasi berkelanjutan.

13) Mengatasi Bias dalam Prompt Engineering: Pendekatan yang Adil.
Untuk mengurangi bias:

Gunakan bahasa netral.
Hindari asumsi stereotip.
Uji dengan berbagai variasi pengguna (usia, budaya, gender).
Sertakan instruksi eksplisit untuk jawaban seimbang.

14) Kendala Etika dan Keamanan dalam Penggunaan Prompt Engineering.
Kendala utamanya:

Penyalahgunaan (misalnya memanipulasi informasi).
Privasi data.
Potensi diskriminasi.
Solusi: menetapkan aturan penggunaan, audit hasil, serta transparansi dalam pemanfaatannya.

15) Masa Depan Prompt Engineering: Tren dan Inovasi yang Diharapkan.
Tren ke depan:

Automated prompt optimization (AI yang menyusun prompt sendiri).
Integrasi dengan multimodal AI (teks, gambar, suara).
Framework standar untuk evaluasi prompt.
Kolaborasi dengan AI yang bisa memahami konteks lebih dalam.

Wiwin Wijaya, 2025

“With the right AI prompts, your business can become more efficient and creative.”

ChatGPT Prompt Engineering