13 - Prompt Engineering Tools and Frameworks: Hands-on with QLLM

Overview of QLLM and other prompt engineering tools

In this chapter, we'll explore prompt engineering tools and frameworks using QLLM, a powerful command-line interface for interacting with multiple Large Language Model (LLM) providers.

13.1 Introduction to QLLM

QLLM is a versatile tool that supports multiple LLM providers, including AWS Bedrock Anthropic's Claude models, OpenAI, and Ollama. Let's start by installing QLLM:

npm install -g qllm

13.2 Basic Prompting with QLLM

Let's begin with a simple prompt using QLLM's ask command:

qllm ask "Write a 100-word story about a time traveler" --max-tokens 150

This command sends a prompt to the default LLM, requesting a short story about a time traveler. The --max-tokens option limits the response length.

13.3 Interactive Prompting

QLLM offers an interactive chat mode, which is useful for iterative prompt development:

qllm chat --max-tokens 150 --provider anthropic --model haiku

This command starts an interactive session with Anthropic's Claude-3 Haiku model. You can refine your prompts in real-time based on the model's responses.

13.4 Streaming Responses

For longer outputs or real-time analysis, QLLM's streaming feature is invaluable:

qllm stream "Explain the concept of prompt engineering in detail" --max-tokens 300

This command streams the response, allowing you to see the output as it's generated.

13.5 Working with Templates

QLLM's template system is powerful for managing complex prompts. Let's create a template for generating product descriptions:

qllm template create

Follow the prompts to create a template named "product-description" with the following content:

name: product-description
description: Generate a compelling product description
provider: anthropic
model: haiku
input_variables:
  product_name:
    type: string
    description: Name of the product
  key_features:
    type: string
    description: Key features of the product
content: |
  Generate a compelling product description for {{product_name}}. 
  Highlight the following key features: {{key_features}}
  The description should be persuasive, emphasize benefits, and include a call to action.
  Keep the description between 100-150 words.

Now, let's use this template:

qllm template execute product-description -v:product_name="EcoBottle Pro" -v:key_features="Insulated, BPA-free, 24oz capacity"

This demonstrates how templates can standardize and simplify complex prompting tasks.

13.6 Template Management

QLLM provides several commands for managing templates:

List all templates: qllm template list
View a template: qllm template view template-name
Delete a template: qllm template delete template-name

While QLLM doesn't have a built-in versioning system for templates, you can manage versions manually by including version information in your template names or descriptions.

13.7 Configuration Management

QLLM uses a configuration file (.qllmrc.yaml) to manage settings. You can view and modify the configuration using the config command:

qllm config --show
qllm config --set-provider anthropic
qllm config --set-model claude-2

In this chapter, we've explored how QLLM can be used as a powerful tool for prompt engineering. We've covered basic prompting, interactive sessions, working with templates, and configuration management. By leveraging QLLM's features, you can streamline your prompt engineering workflow, making it easier to develop, test, and refine prompts for various applications.

Remember, effective prompt engineering is an iterative process. Use QLLM to experiment with different prompts, analyze outputs, and continuously improve your results. Happy prompting!

Qllm is available at https://github.com/quantalogic/qllm