From the course: Integrating AI into the Product Architecture

Prompt engineering techniques to improve LLM output

From the course: Integrating AI into the Product Architecture

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Prompt engineering techniques to improve LLM output

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- [Instructor] Have you ever asked ChatGPT a question and gotten a completely useless answer? Or maybe you got an answer that is technically correct, but missed the point entirely? That's the difference prompt engineering can make. Prompt engineering is the critical skill of crafting effective instructions that help LMS produce accurate, relevant, and useful outputs. Think of it as learning to speak the AI language. The better your prompts, the better your results. So instead of vague requests like, "Explain AI" try something specific. "Explain supervised learning algorithms to a product manager who needs to understand trade-offs between model accuracy and inference speed." See the difference? Let's start by breaking down what makes a great prompt. Every effective prompt has four key components. Clear instructions or what you want. Context, which is background information. Well-structured input data, and output specifications, like how you want the response to be formatted. One approach I have found particularly effective is using templates and delimiters. Try using triple codes to separate these different prompt components. The sandwich technique, that is repeating key instructions at the beginning and end, works wonders for reinforcement, especially with complex tasks. Beyond basic prompting, there are advanced techniques that can transform your results. Role-based prompting can dramatically improve outputs. By instructing the model to act as an experienced data scientist reviewing a machine learning pipeline, you can provide important context that shapes its response, style and focus. Active prompting is where you instruct the LM to ask clarifying questions before attempting to solve a problem. This is particularly valuable when requirements are ambiguous or when precision matters greatly. Like in legal or medical context. Knowledge generation asks the model to list relevant facts before answering, which dramatically reduces hallucinations in technical domains. This technique essentially forces the model to study before answering, creating a stronger foundation for its response. Self consistency helps you generate multiple answers, and find the most reliable one. I've seen this reduced errors by up to 30% in complex reasoning tasks. For example, you might ask the model to solve a problem three different ways. Then compare the answers to identify the most consistent solution. Tree of thought prompting allows the model to explore multiple reasoning paths simultaneously. Rather than forcing linear thinking, it encourages the model to consider different approaches, and select the most promising one. Much like how humans solve complex problems. Remember, great prompt engineering is iterative. When integrating LLMs into products, always AB test different prompt structures. Even small improvements in prompt design can yield significant gains in output quality. Consider metrics like response relevance, accuracy, completeness, and user satisfaction in your evaluation. Handling edge cases is crucial for production systems. Develop prompts that explicitly address potential biases, build in fact-checking steps, and include instructions for handling uncertainty, such as "If you're unsure about something, clearly indicate this rather than guessing." Security considerations are also important. Be aware of prompt injection attacks where malicious users attempt to over idea carefully crafted prompts. Using clear delimiters and validation steps can help mitigate these risks. The key takeaway, prompt engineering isn't just a technical skill. It's the bridge between human intent, and AI capability. Master it and you'll unlock the true potential of LLMs in your product architecture.

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