1. Generating text with language models
Hello! Welcome to this video on generating text with language models.
2. Sending prompts to language models
Let's recap the key concepts that form the foundation of our interaction with language models. We start with the prompt - this is the input text or instruction that we send to the model, like when we ask it to explain a concept or write some code.
3. Sending prompts to language models
The model then processes our prompt and generates what we call a completion, which is the actual text output. For example, if our prompt is 'Explain AWS Lambda,' the completion will be the model's generated explanation.
4. Sending prompts to language models
Finally, what we get back is called the response - it's the complete response that contains both our completion text and useful metadata like token usage. It's just like having a conversation: we send a clear instruction, get back an answer, and see some details about how that answer was generated.
5. Applying basic prompt engineering techniques
Let's explore some techniques that help us get better results when working with language models. First, we have our core techniques: using clarity and specificity in our instructions, like when we ask to 'Explain the benefits of exercise in simple terms' - notice how clear and direct that is. We can enhance this by adding role assignments, such as 'You are a nutritionist,' which frames the response with expert context. We also use formatting instructions to shape our outputs, like requesting bullet points. Understanding these techniques helps us get exactly the kind of response we need.
6. Advanced text parsing techniques
When working with language models, responses can come in different formats and sometimes be unpredictable. That's why we need robust parsing techniques. First, we check if the key data we need exists. If something's missing, we handle it gracefully instead of letting our code crash. We also need to handle more complex situations, for example with models like Nova that nest information deeply in the response structure. By implementing these checks, we make sure our applications can reliably process responses, even when they don't come exactly as expected. This is particularly important in production environments where we can't afford our applications to break because of unexpected response formats.
7. Response processing and text handling
Moving beyond basic parsing, we often need to handle special cases in our text responses. Model responses may contain special characters that appear incorrectly, like symbols or accents. Encoding using specific formats such as UTF-8, followed by decoding, ensures that text displays properly. For lengthy responses, truncating can be a way to help limit the output.
8. Category extraction with Bedrock
Organizations regularly need to classify various text data - from reviews to documents to communications. Traditional systems often misclassify content that requires contextual understanding. Bedrock's AI models excel at recognizing nuance in language. Our implementation uses structured prompts with predefined categories and clear instructions for single-category selection. This approach ensures consistent categorization across various text types.
9. Category extraction with Bedrock
Let's see how to implement this. By providing a list of valid categories and explicit instructions for single-category selection, we ensure consistent and accurate categorization. Here, we're using Nova's text model, which excels in structured classification tasks. The prompt format guides the model to respond with exactly one category, making response processing straightforward.
10. Let's practice!
Let's practice these prompt engineering techniques with some exercises!