Rostyslav Chayka: Prompt Engineering для проєктного менеджменту (Advanced) (UA) LemBS AI PM School 2025 Website – https://lembs.com/aipmschool Youtube – https://www.youtube.com/startuplviv FB – https://www.facebook.com/pmdayconference+

2. AI in Project Management
Rostyslav Chayka
LemBS founder&CEO

3. #9. Advanced Prompt Engineering

4. ChatGPT Models

5. ChatGPT Models

6. GPT-4o (omni)
● Live multimodal assistants: voice agents, real-time screen-sharing copilots,
meeting transcription with instant insights.
● Complex cross-modal reasoning: product-design feedback on an image +
spec sheet; data-visual + text Q&A.
● Premium customer chatbots where nuance, tone and brand consistency
matter.

7. o3
● Deep analytical reports: multi-step proofs, advanced coding help,
graduate-level STEM tutoring.
● Visual reasoning without images: describing diagrams in text or solving word
problems that need structured logic.

8. o4-mini
● High-volume QA or summarisation where latency and cost are critical.
● Lightweight internal chat assistants embedded in productivity apps.

9. o4-mini-high
● Mid-tier coding copilots that need better test-case reasoning than o4-mini but
still cheaper than o3.
● Batch-processing pipelines (e.g., nightly document classification) where a
small quality boost cuts downstream errors.

10. GPT-4.5
● Long-form creative generation: articles, marketing copy, storyboards; more
“flair” than o-series.
● Knowledge-base chatbots that must reference the latest public data.
● Research assistants combining retrieval-augmented generation with large
context windows.

11. 4o-mini
● Mobile or IoT multimodal use-cases (image-to-text captions, quick voice
replies) where millisecond latency matters.
● Prototype apps that need basic vision + text reasoning on a tight token
budget.

13. Prompt Engineering

14. Prompt Engineering Best Practices
● Effective Communication
● Access Quality Data
● Continuous Interaction
● Avoid Sensitive Information
● Restart When Necessary
● Clarity and Specificity
● Consider Context and Goals
● Use Hints and Instructions
● Test and Refine Prompts

15. Prompt Engineering Base Template
● Role / Person
○ You are experienced PM, act as seasoned scientist, ..
● Task
○ Give me 10 ideas about .. , Analyse and summarize text .., List top 5 ways to ..
● Context
○ Previous conversation attached.., Specification is below..
● Output format
○ Output as a table with the following fields:.., Output as a list

16. Prompt Engineering Techniques
● Zero-shot Prompting
● Few-shot Prompting
● Instruction-based Prompting
● ReAct (Reason + Act) Prompting
● Self-consistency
● Retrieval-Augmented Prompting (Context Injection)
● Chain-of-Thoughts (or Multi-Step Prompting)
● Self-Critique / Self-Reflection Prompting
● Persona or Role-Based Prompting
● Instruction Tuning & Fine-Tuning (High-Level)

17. Zero-shot Prompting
Definition
● Asking the model to perform a task without providing any examples.
Example
● “Explain the concept of agile project management in simple terms.”
Use Cases
● Quick, straightforward requests where you trust the model to generalize from
its training.

18. Few-shot Prompting
Definition
● Providing a small number of examples in the prompt to guide the model’s
behavior and outputs.
Example
● You want short, friendly emails. You might include 1–3 sample emails
showcasing the desired style and format, then request a similar output.
Use Cases
● Tasks that benefit from demonstrating the desired style or format (e.g.,
summarizing text, writing short intros, etc.).

19. Instruction-based Prompting
Definition
● Giving the model explicit instructions about what you want, how to format it, and what
constraints to follow—often in bullet points or a step-by-step form.
Example
● “Write a 200-word executive summary that highlights project scope, timeline, and budget.
Use bullet points for each main idea, and avoid any jargon.”
Use Cases
● When you need a predictable structure or specific style (e.g., executive summaries, risk
registers).

20. Persona or Role-based Prompting
Definition
● Assigning a specific persona or role to the model to shape the style, depth, or domain
expertise of its response.
Example
● “You are a senior project manager with 15 years of experience in IT infrastructure. Provide a
high-level project risk assessment for a data center migration.”
Use Cases
● Crafting content that needs a specific perspective or tone (e.g., a technical specialist vs. a
marketing executive).

21. Persona Pattern
Act as [Role, Experience]
● Act as seasoned scientist in Medicine with 5 years of experience
Your expertise
● You excel in finding information about .. in scientific papers
Your approach
● Detail oriented with extended verification
Case
● Please find all information about ..

22. Multi-Persona Pattern
● Look at situation from different point of views
○ [Role 1]: This role focus
○ [Role 2]: This role focus
○ [Role 3]: This role focus
● Each role format

23. Audience Persona Pattern
Who is the audience of Output ?
● Write email for the Company CEO
● Write presentation for the representatives

24. Interview Pattern
● You ask LLM to gather all necessary information from you
● Then generate the output

25. Alternative Approaches Pattern
● Please propose a couple of alternative approaches
● Different audiences, focuses, ..

26. Meta-prompting
● Please propose prompt for creating document about ..

27. Game Play Pattern
You describe game or role case
● Let’s play in game to ..

28. ReAct (Reason + Act) Prompting
Definition
● Combining a “reasoning” section (where the model explains or ‘thinks aloud’) with an “action” section
(where it provides the final answer). The aim is to keep reasoning internal to help the model stay
focused, but in some open models you can guide the process by providing a reasoning framework.
Key Idea
● In systems where you can partially hide the reasoning, you ask the model to reason internally (or at
least separate from the final answer) before it produces the final user-facing output.
Use Cases
● Complex problem-solving steps, where you want to maintain clarity between the AI’s private thought
process vs. the final, polished response.

29. Self-consistency
Definition
● Generating multiple outputs from the same prompt and then combining or choosing
the best answer based on a consensus or majority vote approach.
Example
● Prompt the model multiple times with the same question.
● Collect a set of possible answers.
● Choose the answer that appears most frequently or is best supported.
Use Cases
● Reducing “hallucinations,” factual errors, and variability in answers—especially for
tasks with a single best answer (e.g., math problems, factual Q&A).

30. Retrieval-Augmented Prompting (Context Injection)
Definition
● Using external knowledge bases or documents to inject context into the prompt. Often
involves searching a database for relevant snippets, then adding that text to your prompt so
the model can ground its answers in verifiable data.
Example
● Prompt: “Based on the following project specs, provide a risk assessment: [Insert relevant
paragraphs from the project documentation].”
Use Cases
● Environments where you have domain-specific knowledge that the model may not contain
from training (e.g., internal company SOPs, project archives, or updated policies).

31. Chain-of-Thoughts Prompting
Definition
● Breaking down complex tasks into smaller sub-prompts and feeding each output into the next
prompt, chaining them together to reach a final result.
Example
● Prompt 1: “List the top five risks for this project based on the data below.”
● Prompt 2: “Now that we have these five risks, create a mitigation plan for each.”
● Prompt 3: “Summarize the mitigation plan in bullet points for an executive audience.”
Use Cases
● Complex workflows that require step-by-step transformations (e.g., generating code, summarizing
large documents, multi-faceted project analyses).

32. Tree-of-Thoughts Prompting
You’re designing a communication plan for five key stakeholders. Use
Tree-of-Thoughts: 1. Identify three messaging approaches (e.g., data-driven
updates, narrative storytelling, risk-focused briefings). 2. For each approach,
outline two communication channels (email, dashboard, meeting). 3. Predict how
each channel-approach pair impacts stakeholder engagement and
decision-making. 4. Eliminate combinations with low engagement potential. 5.
Select the top three channel-approach combinations and draft a one-sentence
sample message for each. Begin by detailing the three messaging approaches.

33. Semantic Filter Pattern
You’re analyzing a batch of 20 raw customer feedback comments.
1. First, list all 20 feedback comments as you received them.
2. Then apply a semantic filter to identify only those comments that:
- Express **negative sentiment** (frustration, disappointment, complaints)
- Mention **usability issues** (navigation, loading speed, confusing labels)
3. Discard any comments that are purely praise, unrelated questions, or feature requests.
4. From the filtered set, return a numbered list of the **actionable negative-usability**
comments only.

34. Self-critique / Self-reflection Prompting
Definition
● Asking the model to critique its own output or reflect on potential mistakes before producing a final
answer.
Example
● Prompt: “Draft a risk register for the attached project plan.”
● Follow-up Prompt: “Review the risk register you just created. Are there any logical inconsistencies or
missing points? If so, correct them.”
Use Cases
● Improving accuracy, particularly in tasks that require careful reasoning or ensuring thoroughness
(e.g., policy analyses, complex planning documents).

35. Emotional Prompting
Imagine you’re a team leader giving a pep talk before a big project kickoff.
– Infuse your message with optimism and confidence.
– Use vivid, uplifting language that stokes excitement and unity (“we,” “together,”
“unstoppable”).
– End with a rallying call that leaves everyone energized.

36. Instruction Tuning & Fine-Tuning
Definition
● Instruction Tuning: Training the model to respond effectively to instruction-like prompts.
● Fine-Tuning: Customizing the model by retraining it on specific data or specific prompt-response
pairs.
Example
● Fine-tuning a model with your organization’s past project lessons learned so it’s more aligned with
your domain or style guidelines.
Use Cases
● When you need the model’s “default” behavior to be closely aligned with your organization’s
terminology, style, or compliance requirements.

37. Tone of Voice

42. Temperature

46. top_p

49. Prompts for Image Generation

50. Image Generation prompts
● Main subject (s)
○ A vintage car parked on a street.
● Physical characteristics
○ A vintage car with red paint and chrome bumpers.
● Artistic Style
○ In a photorealistic style
● Medium
○ oil painting, pencil sketch, digital art, etc
● Environment or location
○ In a busy urban street with skyscrapers in the background.
● Lighting condition
○ "soft morning light" or "dramatic shadows"

51. Image Generation prompts
● Action or pose
○ The person is standing on a mountain peak, looking into the distance.
● Mood or Atmosphere (Emotional tone)
○ "In a joyful and lively atmosphere" or "In a mysterious, fog-covered forest."
● Color scheme
○ "With a pastel color palette" or "Dominated by vibrant reds and oranges."
● Angle of view
○ "top-down," "wide-angle," or "close-up."
● Composition details
○ "rule of thirds" or "centered composition"

52. Image Generation prompts
● Additional details
○ "With butterflies flying around" or "With a steaming coffee cup on the table."
● Time period or Cultural context
○ "In the style of 1920s Art Deco"
● Important keywords / Descriptive phrasing
○ "A serene, misty landscape with rolling hills" or "A futuristic city with towering skyscrapers and
flying cars."
● Modifiers
○ "highly detailed," "minimalist," or "dreamlike."

53. Video Generation

54. Internet Search

55. Internet Search
● OpenAI
● Gemini
● Perplexity

56. Deep Research

57. Deep Research
● OpenAI: Deep Research: Plus(10 prompts), Pro(120 prompts)
● Gemini: Free
● GROK
● Perplexity
● Manus

58. Data Analysis

59. Data Analysis
● Claude

60. Logo Generation

61. Logo Generation
● logoai.com/logo-maker
● logome.ai
● looka.com
● design.com/ai-logo-generator
● www.canva.com/dream-lab
● logoai.ai/app
● theresanaiforthat.com/logo-design/?ref=suggest

62. Creating Diagrams, Charts, Infographics,
Reports

63. Кейси використання
● Презентації
● Aналіз Ui/Ux
● Швидка генерація прототипів інтерфейсу
● Опис текстом діаграми з генерацією коду діаграми в md форматі.
● Завантажити існуючий інтерфейс і провести аналіз Ui/Ux.

64. Video tutorials and
animations with AI

65. Video tutorial and animations
1. https://Elai.io text to video + avatars + голос, зроблено українцями і відео в пості саме
зроблено Elai.
2. https://Otter.ai - робить текстовый транскрипт з відео або аудіо. Наприклад для субтитрів.
3. https://shuffll.com/ - даєте текст, бренд айдентику та відео (або записуєте наживо), а
отримуєте готове відео за декілька хвилин.
4. https://www.poised.com/ - коуч з публічних виступів, аналізує відео або зустрічи.
5. https://www.synthesia.io/ - досить потужний для створення вигаданих, або власних аватарів
та створення навчальних, коучингових, презентаційних відео з ними для різних процесів.
6. https://www.mirageml.com/ - створення 3D об'єктів за текстом.
7. https://www.supercreator.ai/ - створює короткі відео з тексту або посилання на текст у блозі
тощо.
8. https://Steve.ai - прикольна ідея, створює прості анімаційні відео на базі тексту, голосу або
скрипту.
9. https://vidyo.ai - створює короткі відео резюме з довгих відео
10. https://hourone.ai/ Automatically turn Icon1 text into videos Icon2 featuring virtual Icon3
presenters
11. https://www.videomakerai.com/ - стилізує відео.
12. https://filmforge.pro/ - створює відео для stories, e-commerce, навчання на основі тексту.
13. https://www.neuralframes.com/ - створює motion відео на основі тексту.
14. https://kaiber.ai/ - створює артистичні відео по тексту. Є безкоштовний план.
15. https://www.flowjin.com/ - створює відео для Youtube або TikTok на онові аудіо контенту. Є
безкоштовний план.
16. https://www.vidvoice.ai/ - синхронний перекладач для відео.
17. https://Pictory.ai - автоматично створює короткі брендовані відео на основі тексту, як блог,
або опису.
18. https://Lumen5.com - автоматично створює короткі брендовані відео на основі тексту, як
блог, або опису. На відміну від Pictory, може зробити нарізку з zoom відео.
19. https://Cutout.pro - прибирає бекграунд фон у відео.
20. https://D-id.com - робить відео з фото, підставляє свій голос або текст щоб озвучити.
21. https://invideo.io/ - автоматично створює короткі відео на основі тексту, як блог, або опису.
Якість контенту для відео досить низка. Нажаль.
22. Https://Research.runwayml.com - стилізація, сторіборд, маски, рендер та налаштування
моделей.

69. Video Avatars (ABU)

73. ChatGPT API

74. AI Agents
● Goal GPT
● Auto GPT
● Make + ChatGPT + External Tools (API) + Communication Channels
● Custom AI Agents

75. Specialized Agents

80. AutoGPT

82. CrewAI

94. Case Study: Project Planning

97. Case Study: Project Progress Report

100. Other cases

104. Replit

107. AI CTO
● Cursor
● Replit
● v0
● Bolt
● Loveable

109. Questions ?

110. Contacts
linkedin.com/in/rchayka
fb.com/rostyslav.chayka
rchayka@gmail.com