Prompting That Works: Getting Real Results from AI

Week 2 of the Tesseract Academy AI Fluency Certification. A practical, no-hype course for non-technical business professionals who want reliable real-work results from AI tools through clear structured prompting, repeatable prompt patterns, verification habits, and reusable prompt libraries.

Who’s This For

This course is designed specifically for busy, non-technical business professionals—including marketers, operations managers, recruiters, analysts, and communicators—who are already using tools like ChatGPT, Claude, or Gemini but want to move past basic trial-and-error. You do not need a background in computer science to participate; you simply need a drive to make AI a reliable, highly productive partner in your daily work.

If you are frustrated by generic, superficial AI outputs, concerned about data privacy, or tired of rewriting prompts from scratch every day, this course offers the practical guardrails you need. We focus on cutting through the hype to deliver concrete, real-world strategies that save time and ensure accuracy.

Whether your goal is to accelerate content creation, automate complex data extraction, or build a shared library of standard prompts for your department, this course bridges the gap between basic tool awareness and elite professional execution.

What You’ll Learn

• Master the Anatomy of a Professional Prompt: Learn to construct prompts using a reliable, structured framework including role, context, task, format, and explicit constraints.
• Deploy Repeatable Work Patterns: Implement battle-tested prompt templates for everyday business tasks, from high-impact marketing drafting to complex document summarization and decision analysis.
• Apply Advanced Reasoning Techniques: Guide AI models through complex multi-step tasks using few-shot learning, chain-of-thought reasoning, and structured output formatting.
• Build a Professional Verification Habit: Master the critical safeguards needed to spot hallucinations, address inherent bias, and securely manage confidential company data.
• Develop Reusable Prompt Libraries: Establish custom instructions and organized prompt repositories to maintain consistency and efficiency across tools like Claude, Gemini, and ChatGPT.
• Seamlessly Integrate AI into Daily Workflows: Transition from sporadic, experimental usage to automated, repeatable processes that shave hours off your weekly workload.

Popular AI Tools You May Use

Throughout this course, learners can apply the same prompting principles in the mainstream AI assistants they are most likely to encounter at work: ChatGPT, Claude, and Gemini. The course also introduces Kimi as a useful alternative to be aware of, especially when teams want to compare how different models handle long documents, structured prompts, or research-heavy workflows.

The goal is not to crown one tool as the winner. Busy professionals should learn to write prompts that travel well across tools, then choose the assistant that fits the task, the organization’s data policies, and the quality of the output.

Frequently asked questions

What is the difference between zero-shot and few-shot prompting?

Zero-shot prompting asks the model to perform a task without examples, relying solely on its training data. Few-shot prompting includes a small number of input-output pairs to guide the model. Few-shot typically improves accuracy for complex tasks, while zero-shot is faster and requires less token usage.

How does temperature affect the output of a large language model?

Temperature controls the randomness of token selection during generation. A low temperature, such as 0.2, produces deterministic and consistent responses suitable for factual queries. A high temperature, like 0.9, increases diversity and creativity, making it useful for brainstorming but potentially less accurate for strict data extraction.

Which large language model is best for coding tasks?

Claude 3.5 Sonnet and GPT-4o are currently top performers for complex coding tasks. Claude excels at long-context reasoning and debugging, while GPT-4o offers strong multilingual support. The best choice depends on specific requirements, such as integration with existing development environments or the need for real-time web browsing capabilities.

What is prompt injection and how can it be prevented?

Prompt injection is an attack where malicious input manipulates the model to ignore its original instructions. Prevention involves strict input validation, separating system prompts from user data, and using sandboxed environments. Regularly testing prompts with adversarial examples helps identify vulnerabilities before deployment in production systems.

How much does it cost to use API access for large language models?

Costs vary significantly by provider and model size. For example, GPT-4o costs approximately $2.50 per million input tokens and $10 per million output tokens. Smaller models like GPT-4o-mini are cheaper, at $0.15 per million input tokens. Most providers offer free tiers for testing, but production usage requires a paid subscription.