1. Applying prompt engineering techniques
Hello! Welcome to this video on applying more prompt engineering techniques.
2. Few shot learning
Few-shot learning is a powerful technique that helps us get better responses from language models. By providing a few carefully chosen examples in our prompt, we show the model exactly what kind of output we're looking for. Think of it as teaching by example - just as we might show someone a few examples of how to format a document before asking them to create their own. This approach reduces ambiguity and helps the model generate more consistent and accurate outputs.
3. Few shot learning with models
Let's look at few-shot learning in practice with AWS service summaries. Notice how we provide two clear examples - Amazon S3 and EC2 - before asking about AWS Lambda. Each example follows the same pattern: service name followed by a concise summary. This consistent structure helps the model understand exactly how we want the information presented. The examples are brief yet informative, showing the model the level of technical detail and length we're expecting in the response.
4. What is structured output formatting?
Structured output formatting helps us guide the model's responses into a clear, organized format. For example, it can be asking the model to follow a specific template with labeled sections like DESCRIPTION and KEY FEATURES. This structure makes the output easier to read and simpler to process in downstream tasks, which makes it ideal for automation and data extraction tasks.
5. Controlling response format
In this example, we're controlling the output in a very structured way to analyze AWS Lambda, another AWS service. Notice how we use specific markers like 'DESCRIPTION:', 'KEY FEATURES:', and 'USE CASES'. We're also explicit about what we want - a description that's exactly 2-3 sentences long, followed by specific features and use cases in a numbered format. These clear markers and format constraints not only guide the model in providing well-organized information but also make it much easier to parse and process the response programmatically.
6. Creating effective prompts
Let's take a look at an example of an effective prompt that follows a few best practices. In this example, we first assign the model a specific role as an AWS technical writer. Then, we provide a concrete example describing the GetItem API, showing exactly how we want the documentation structured with its purpose, parameters, and return values. Finally, we use this template to request documentation for a new API, PutItem. By layering these techniques - role assignment, example-based learning, and structured formatting - we create a prompt that consistently produces clear, well-organized API documentation.
7. Let's practice!
Let's practice all these advanced techniques with some exercises!