Creating LLM Agents with Instructions

Introduction

Artificial Intelligence (AI) agents, particularly those powered by Large Language Models (LLMs), have revolutionized the way we interact with technology. These agents can understand and generate human-like text, answer questions, and perform a wide range of tasks. In this article, we'll explore the anatomy of an AI agent and guide you through the process of creating an LLM agent with instructions.

The Anatomy of an AI Agent

An AI agent, especially one based on an LLM, typically consists of several key components:

Basic

1. Base Model: The foundational LLM trained on vast amounts of text data.
   For example ChatGPT, Claude Sonnet, Gemini, etc.
2. Prompts: Instructions given to the model to guide its responses.
   For example: 'You are a helpful assistant.'
3. Context: Context given to the model to guide its responses.
   For example: 'Call the user Fred, he loves cats, it's Monday 2024-09-02'

Advanced

4. Tools: Tools for context enchrichment or taking actions.
   Examples: Search web, Generate images, Add a task to Jira, etc.
5. Fine-tuning: Additional training on specific datasets to specialize the model.
6. Output Processing: Mechanisms to refine and format the model's output.
   Examples: Format as Markdown, profanity filters, tone adjustments
7. Memory: Systems to retain context across multiple interactions.
   Example: Fred's brithday is on 8th January, Kelly is his boss.

Creating an LLM Agent with Instructions

Here's a step-by-step guide to creating a basic LLM agent:

1. Select a Base Model: Choose an appropriate LLM (e.g., GPT-4, Claude, or Gemini) based on your requirements and available resources.
2. Design the Prompt Structure: Define the agent's role and behavior
3. Craft Clear Instructions: Write concise, specific instructions that guide the model's behavior and responses. For example:

System: You are a helpful assistant specializing in software development. Provide concise, accurate answers to coding questions. Use markdown for code snippets.

4. Provide Relevant Context: Include any necessary background information or constraints for the agent's task. For example:
5. Test and Refine: Interact with your agent, analyze its responses, and iteratively refine your prompts and instructions to improve performance.
6. Implement Basic Error Handling: Develop simple mechanisms to handle unexpected or inappropriate responses from the model.

Consider Ethical Guidelines: Ensure your agent adheres to ethical standards and respects user privacy and safety.

By focusing on these basic components - the base model, prompts, and context - you can create a functional LLM agent that serves a specific purpose or domain. As you become more comfortable with this process, you can explore more advanced features like tools, fine-tuning, output processing, and memory systems to enhance your agent's capabilities

Prompt Examples and Scenarios

Let's look at some prompt structures and scenarios:

Scenario 1: Customer Service Agent

In this scenario, we'll create an AI agent that serves as a customer service representative for a tech company. This example demonstrates how to structure prompts for handling customer inquiries and providing product support.

The prompt structure includes:

1. System instructions defining the agent's role and objectives
2. Context providing essential company information
3. A sample user inquiry to illustrate the interaction

Here's the prompt structure for a customer service AI agent:

System: You are a helpful customer service agent for TechGadgets Inc. Your goal is to assist customers with their inquiries and resolve their issues politely and efficiently.

Context: TechGadgets Inc. sells smartphones, laptops, and tablets. We have a 30-day return policy and a 1-year warranty on all products.

User: I bought a laptop last week, but it's not working properly. What should I do?

Scenario 2: Personal Fitness Trainer

In this scenario, we'll create an AI agent that acts as a personal fitness trainer. This example demonstrates how to structure prompts for providing personalized workout advice and motivation.

System: You are a knowledgeable and motivating personal fitness trainer. Your goal is to provide tailored workout advice and encouragement to help clients achieve their fitness goals.

Context: You have access to basic information about the client's age, weight, height, and fitness goals.

User: I'm 35, 180 lbs, and 5'10". I want to lose weight and build some muscle. What kind of workout routine do you recommend?

Scenario 3: Language Tutor

In this scenario, we'll create an AI agent that functions as a language tutor specializing in Spanish. This example demonstrates how to structure prompts for providing language learning assistance, including grammar explanations and conversation practice. The prompt structure includes system instructions defining the tutor's role, context about the student's level, and a sample user inquiry to illustrate the interaction.

Here's the prompt structure for a language tutor AI agent:

System: You are a patient and encouraging language tutor specializing in teaching Spanish. Your goal is to help students improve their Spanish language skills through conversation practice and grammar explanations.

Context: You are working with an intermediate-level student who wants to improve their conversational skills.

User: Can you help me practice using the subjunctive mood in Spanish?

Scenario 4: Financial Advisor

System: You are a reliable financial advisor with expertise in personal finance, investments, and retirement planning. Your goal is to provide sound financial advice tailored to each client's unique situation.

Context: You have access to general information about various investment options, tax laws, and financial planning strategies.

User: I'm 40 years old and want to start planning for retirement. Where should I begin?

Scenario 5: Recipe Assistant

System: You are a creative and knowledgeable recipe assistant. Your goal is to help users find, modify, or create recipes based on their preferences, dietary restrictions, and available ingredients.

Context: You have access to a wide range of recipes and nutritional information for various ingredients.

User: I'm looking for a vegetarian dinner recipe that's high in protein and low in carbs. What can you suggest?

These scenarios showcase how AI agents can be tailored for various specialized tasks, demonstrating the versatility of LLM-based systems when provided with appropriate context and instructions.