Skill Guide

Prompt engineering and prompt chaining across text, image, and video models

Prompt engineering is the systematic design of textual or multimodal inputs to guide generative AI models toward producing specific, high-quality outputs, while prompt chaining is the architectural pattern of linking these engineered prompts in sequence, where the output of one model call becomes the input for the next, to accomplish complex, multi-step tasks.

This skill directly translates to operational efficiency and competitive advantage by maximizing the accuracy, creativity, and utility of expensive API calls to frontier models, reducing manual post-processing time, and enabling the automation of sophisticated content creation and data analysis workflows that were previously impossible.

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How to Learn Prompt engineering and prompt chaining across text, image, and video models

1. Master the anatomy of a prompt: Understand core components like role definition, instruction, context, input data, and output format (e.g., R-C-I-F-O). 2. Learn fundamental techniques for text models: Zero-shot, few-shot, and chain-of-thought (CoT) prompting for reasoning tasks. 3. Grasp the basics of multimodal prompting: How to structure a simple prompt for a text-to-image model (e.g., specifying subject, style, lighting, and medium).

1. Implement structured output control: Use XML tags, JSON schema enforcement, or explicit formatting instructions (e.g., 'Output your answer as a JSON object with keys "analysis" and "recommendation"'). 2. Design basic prompt chains: Create a 2-3 step chain where a text model generates a detailed description, an image model renders it, and a final text model evaluates the output. Avoid the common mistake of poorly defined interfaces between steps. 3. Apply iterative refinement: Use a systematic 'generate -> evaluate -> refine prompt' loop for image generation, focusing on negative prompts and parameter tuning (CFG scale, steps).

1. Architect complex, stateful chains: Design chains with error-handling, branching logic (using conditional prompts), and memory management (summarizing previous steps). 2. Optimize for cost and latency: Engineer prompts that minimize token count while preserving quality, and chain models strategically (e.g., using a smaller, faster model for initial filtering before a more expensive one). 3. Develop cross-modal prompting strategies: Create unified prompts that instruct models on how to interpret outputs from other modalities (e.g., 'Based on the key visual themes in the image {image_description}, generate a matching video script').

Practice Projects

Beginner

Project

Brand Asset Generation Pipeline

Scenario

You need to generate a consistent set of social media images (hero image, icon, and background pattern) for a fictional eco-friendly sneaker brand.

How to Execute

1. Define the brand's visual style in a master prompt (e.g., 'minimalist, sustainable, earth tones, high texture'). 2. Use a single text-to-image model (like DALL-E 3) with iterative prompt variations to generate the hero image, saving the final prompt. 3. For the icon and pattern, modify the master prompt with specific constraints (e.g., 'vector logo of a leaf integrated with a sneaker silhouette' and 'seamless repeating pattern of small leaf outlines'). 4. Document the prompt variants used and their outcomes.

Intermediate

Project

Automated Content Repurposing Chain

Scenario

Transform a 10-minute technical blog post into a 30-second animated explainer video summary.

How to Execute

1. **Text Extraction & Summary:** Use a text LLM (e.g., GPT-4) with a prompt to extract the core 3-act narrative and 3 key facts. 2. **Storyboard & Script Generation:** Chain the summary to a second prompt that generates a 5-scene storyboard in JSON format (scene description, narration, visual keywords). 3. **Asset Generation:** Use the visual keywords from each scene to batch-generate keyframes via a text-to-image API. 4. **Assembly & Final Prompt:** Use a video model (like Runway Gen-2 or Pika) with the generated keyframes as image inputs and the narration as a text prompt to stitch the final clip. Implement a review loop to adjust prompts based on video output coherence.

Advanced

Project

Multi-Agent Research & Synthesis System

Scenario

Conduct a competitive analysis on 'the future of urban mobility' by simulating a team of expert personas (economist, engineer, urban planner) debating and producing a consolidated report.

How to Execute

1. **Design Agent Roles:** Create distinct system prompts for each expert persona, defining their background, bias, and evaluation criteria. 2. **Implement a Debate Chain:** Use a controller prompt to manage a multi-turn chain: Pose a research question -> get Economist's response -> feed it to Engineer for critique -> feed both to Urban Planner for synthesis. 3. **Integrate Retrieval (RAG):** Augment each agent's context window with live search results or document snippets for factual grounding. 4. **Meta-Prompt for Synthesis:** Use a final, executive-level prompt that reviews the entire debate transcript and produces the consolidated report, citing points of agreement and conflict. Implement token usage monitoring across the chain.

Tools & Frameworks

Software & Platforms

OpenAI API / PlaygroundLangChain / LlamaIndexStable Diffusion WebUI (A1111)Runway Gen-2

OpenAI's platform for testing prompt variations with fine-grained parameter control. LangChain is essential for building and orchestrating prompt chains programmatically, managing memory, and connecting to external tools. The Stable Diffusion WebUI is the industry standard for local, iterative image prompt engineering with full control over negative prompts and samplers. Runway represents the frontier of accessible video generation models for prompt-to-video work.

Mental Models & Methodologies

CRISPE FrameworkTree of Thoughts (ToT)Multi-Persona PromptingPrompt Templating

CRISPE (Capacity, Role, Insight, Statement, Personality, Experiment) is a robust framework for decomposing complex prompt requests. Tree of Thoughts is an advanced reasoning technique for complex problem-solving where the model explores multiple branches of thought. Multi-Persona prompting instructs the model to simulate a panel discussion or critique its own output from different viewpoints. Templating involves using variables (e.g., {topic}) within prompts for reusable, chainable components.

Interview Questions

Answer Strategy

The interviewer is testing your ability to architect a multi-stage pipeline and handle real-world data imperfections. Structure your answer: 1. **Extraction Phase:** Use a vision-capable model (like GPT-4V) with a precise prompt to extract text and describe visual elements from each screenshot. 2. **Cleaning & Structuring Phase:** Chain the raw output to a text model with a prompt focused on parsing, deduplicating, and formatting the extracted data into a clean JSON array. 3. **Validation Phase:** Add a third step using a model prompt to act as a data validator, flagging incomplete records or mismatched logo-to-company associations. Emphasize error handling at each handoff.

Answer Strategy

This tests your methodical approach to iterative prompt engineering. Answer: I would first isolate variables by using a base prompt with a seed number to establish a baseline. Then, I would apply a controlled ablation study: 1. Test variations on the core descriptor (e.g., 'cyberpunk hacker' vs 'young cyberpunk hacker with green hair and a scar'). 2. Experiment with style and medium keywords (e.g., 'cinematic screenshot, 35mm film' vs 'digital art'). 3. Systematically use the `--sref` (style reference) or `--cref` (character reference) parameters with a generated character sheet to enforce consistency. The key is changing one parameter at a time and logging the results.