Vibe Coding & Prompt Engineering Learning Module

1.1 What is Vibe Coding?

Vibe coding is a conversational approach to software creation. Instead of writing code line‑by‑line, a user describes the desired functionality in natural language and an AI assistant generates and iteratively refines the code. An early adopter described vibe coding as "having a conversation with an AI to build software" where the AI acts like a tireless technical co‑founder.

This paradigm frees users from syntax and low‑level debugging and lets them focus on articulating their vision. Success depends on clear communication and a collaborative mindset.

1.2 What is Prompt Engineering?

LLMs like GPT‑4, Claude or Gemini convert textual prompts into responses. Prompt engineering refers to the design and optimization of those inputs. The MIT Sloan AI Hub defines prompts as "conversation starters" and notes that effective prompts guide the AI's output quality.

Prompt engineering therefore combines clarity, context, constraints, and examples to coax AI models into producing the desired result.

1.3 How Vibe Coding and Prompt Engineering Relate

Vibe coding treats AI as a creative partner; prompt engineering supplies the instructions that shape that partnership. A well‑engineered prompt functions as the interface between the human's intention and the model's implementation. When building software via conversation, one must continually craft, test and refine prompts to direct the AI toward the right logic, structure and user experience.

3.1 Definition of a Prompt

A prompt is the text (or multimodal input) that instructs an AI model. MIT Sloan states that prompts can range from a single phrase to multiple paragraphs. Analytics Vidhya describes prompts as "a detailed description of desired output expected from the model," forming the interaction between user and AI.

3.2 What is Prompt Engineering?

According to Analytics Vidhya, prompt engineering is a practice in which textual inputs describe what the AI should do. Because the task description is embedded in the input, the model flexibly generates outputs in different forms. Palantir emphasises that clarity, specificity and contextual relevance are key strategies for effective prompting.

3.3 Why It Matters

As generative models become more capable, prompt engineering is critical for harnessing their potential. The Lakera guide notes that prompt engineering helps improve output quality, control tone and structure, and mitigate safety risks. By mastering this skill, developers and non‑developers can leverage AI systems more effectively.

4.1 From Writing Code to Describing Solutions

Vibe coding represents a fundamental shift in software development: from writing syntax to articulating desired outcomes. A practitioner notes that you no longer need to learn Python or JavaScript; instead you must articulate your vision in clear, structured natural language. This approach liberates creators from low‑level details and emphasises collaboration.

4.2 Pair Programming with AI

The vibe coding process mirrors pair programming: the human acts as navigator, while the AI "drives." This partnership involves iterative dialogue: the user describes a concept, the AI implements a basic version, the user provides feedback, and the cycle repeats. Effective vibe coding requires balancing creative direction with guidance: being specific enough to convey intent but flexible enough to leverage the AI's capabilities.

4.3 Choosing Your AI Coding Assistant

Numerous AI coding assistants exist. Important evaluation criteria include conversational fluency, support for desired languages, ability to explain generated code, integration with development environments, and deployment options. Because tools evolve quickly, the focus should be on evaluating capabilities rather than specific names.

6.2 Common Prompt Types

6.3 Prompt Variables

Key variables that affect responses include the model's role, the temperature (controls randomness), specificity of instructions, and tone. The training agenda encourages learners to experiment with these variables when refining prompts.

7.1 Zero‑Shot and Few‑Shot Prompting

Zero‑shot prompting uses a direct instruction without examples. It can be effective for straightforward tasks but may struggle with nuanced outputs. Few‑shot prompting provides a handful of examples to anchor the response. Analytics Vidhya notes that few‑shot prompts allow the model to glean insight from the demonstrations and improve performance.

7.2 Chain‑of‑Thought (CoT) Prompting

CoT prompting encourages the model to generate intermediate reasoning steps. Analytics Vidhya explains that CoT "allows the model to achieve complex reasoning through middle reasoning steps," creating chains of reasoning that foster better understanding and outputs. CoT often combines elements of few‑shot prompts and stepwise instructions.

7.3 Tree‑of‑Thought (ToT) Prompting

Tree‑of‑thought prompting extends CoT by exploring multiple reasoning branches. This technique instructs the model to consider several paths before converging on a solution. ToT can be paired with evaluation metrics that select the best branch.

7.4 Skeleton‑of‑Thought and Chain‑of‑Emotion

Skeleton‑of‑thought prompts ask the model to outline its reasoning structure before fleshing out details, promoting coherence. Chain‑of‑emotion prompts guide the model to consider emotional states or empathy in its responses. These are emerging techniques that can enhance storytelling and user engagement.

7.5 Generated Knowledge Prompting

Some approaches generate external knowledge or context before answering a question. For example, instruct the model to list relevant facts and then provide an answer using those facts. This can improve accuracy and reduce hallucinations.

8.1 Smart Prompting for Learning and Creativity

Prompt engineering supports knowledge work, education and ideation. The Vibe Coders training agenda suggests using prompts for teaching, summarizing and revising content, and for creative tasks like storytelling, brainstorming and ideation.

8.2 Tool‑Specific Prompt Engineering

Different platforms respond differently to prompts. The training agenda highlights adjusting prompts for ChatGPT, Claude, Notion AI, and other tools. Factors include character limits, interface styles, and context windows. It's advisable to test prompts across tools and adapt them accordingly.

8.3 Multi‑Step Task Design

Complex tasks can be handled by layering prompts: ask, check, revise. For instance, create a summary, then ask the model to critique it, and finally refine the output. Layering prompts improves accuracy and helps catch errors.

8.4 Domain‑Specific Applications

9.1 Prompts as Interfaces

In vibe coding, prompts become the interface between the user and the application. Training materials describe using prompts to simulate calculators, forms and assistants. This allows non‑programmers to build lightweight tools without traditional coding.

9.2 Logic‑First, Syntax‑Light Mindset

Vibe coding emphasises logic and product vision over syntax. You focus on what the tool should do for users, not how to implement it in a specific language. The AI handles code generation and integrates user feedback.

9.3 Layering Prompts for Multi‑Step Apps

Building more complex applications often requires a sequence of prompts: gather requirements, generate code, test functionality, and refine. For example, to build a budget tracker, you might prompt the AI to generate an initial database schema, then ask it to implement CRUD functions, and finally request a user interface description. Each step provides context for the next.

9.4 Example Projects

The vibe coding article highlights real users who built an inventory tracker and a specialized writing tool through conversational interactions with AI. Learners are encouraged to identify personal projects where vibe coding adds value and to practice by describing the project's core purpose in 1–2 sentences.

10.1 Formatting Techniques & Reasoning Scaffolds

Modern models respond to carefully formatted prompts. The Lakera guide notes that in 2025, prompt engineering spans formatting techniques, reasoning scaffolds and role assignments. Reasoning scaffolds such as chain‑of‑thought and tree‑of‑thought help models break down complex problems.

10.2 Role Assignments and Personas

Assigning a role or persona can significantly change the model's style and domain knowledge. Use roles to align the output with the desired voice (e.g., "You are a cybersecurity analyst..."). Combining roles with constraints (tone, length, style) enhances control.

10.3 Adversarial Prompting & Security

Prompt engineering isn't solely a usability tool; it can also be exploited. The Lakera guide warns that adversaries can bypass safety guardrails by reframing questions, revealing that the line between aligned and adversarial behavior is thin. Understanding prompt injection techniques and defence strategies is thus an advanced skill.

10.4 Multimodal Prompting

Emerging models accept images, audio or video as inputs. Prompt engineering for multimodal models involves combining textual instructions with images (e.g., "Describe this chart and suggest improvements"). When designing multimodal prompts, clearly specify the task for each modality and desired format of the response.

10.5 Agentic & Tool‑Using Prompts

Future AI systems are agentic; they can call external tools, search the web and perform actions. Prompt engineering for agents involves specifying when and how to use tools. For example, instruct the model: "If information is missing, search the web and cite sources."

11.1 Test and Refine

Effective prompt engineering is iterative. Palantir recommends experimenting with different prompt structures, evaluating outputs, and refining based on quality. Start with a basic prompt and gradually add context, examples or constraints until the output meets your needs. Tools like scoring rubrics, human review or automated metrics can help evaluate effectiveness.

11.2 Feedback Loops & Meta‑Prompts

Use the model's feedback to improve prompts. Ask the AI to critique its own response, identify errors or propose improvements. This creates a meta‑prompting loop where the model assists in refining its instructions. System prompts—behind‑the‑scenes instructions that set global behavior—can also guide tone and safety.

11.3 Manage Bias and Hallucinations

Prompts should minimize the risk of generating misleading information. Provide constraints like "cite real sources" or "if unsure, say you don't know" to reduce hallucinations. Evaluate outputs critically and cross‑check facts against reliable references.

11.4 Document and Reuse Prompts

Maintaining a prompt portfolio—a collection of effective prompts and their outcomes—helps you reuse successful patterns and learn from failures. The training agenda encourages learners to document their top prompts with title, use case and output sample.

12.1 Avoid Hallucinations and Misuse

Responsible prompting includes avoiding ambiguous or leading questions that encourage hallucinations. Palantir stresses being clear and concise, breaking tasks into smaller parts, and incorporating constraints. It also advises using negative instructions to limit unwanted outputs.

12.2 Equity and Bias Considerations

AI models may reproduce societal biases. Prompt engineers should be aware of sensitive attributes and avoid making high‑impact decisions based on race, gender, or other personal traits. When working on classification or recommendation tasks, prompts should focus on objective criteria.

12.3 Privacy & Data Protection

Do not include sensitive personal information or confidential data in prompts. Follow privacy guidelines and, when using user‑generated data, ensure appropriate consent and anonymization.

12.4 Safety & Security

Adversarial prompts can compromise AI systems. The Lakera guide underscores that prompt engineering can be a potential security risk when exploited. Understand prompt injection attacks and implement defenses such as input sanitization and output monitoring.