LANGCHAIN — Prompt Engineering
Imagination is more important than knowledge. For knowledge is limited, whereas imagination embraces the entire world, stimulating progress, giving birth to evolution. — Albert Einstein
Generating usable text with AI involves addressing various challenges and employing effective solutions. In this tutorial, we will explore practical code examples and insights into generating usable text with Large Language Models (LLMs), focusing on prompt engineering, task segmentation, performance evaluation, and security considerations.
1) Prompt Engineering
Example of a well-formed prompt for Summarization:
prompt = """Please analyze the extensive details provided about this food product, including the ingredients, benefits, instructions, and customer reviews. Extract the most crucial information, taking into account various aspects such as nutritional value, taste, user experiences, and overall market trends. Create a concise summary suitable for product packaging, striking a balance between brevity and capturing the product's multifaceted qualities. Also, ensure that the summary caters to diverse consumer preferences and aligns with current industry standards. It should be versatile enough to meet the expectations of both seasoned food enthusiasts and those new to culinary experiences. Additionally, incorporate any notable advancements in food science and technology to highlight the product's innovative features."""2) Task Segmentation
Example of breaking down complex tasks for movie recommendations:
prompt1 = """You are a language model assisting a web platform in recommending movies to users based on their preferences. Your objective is to present the movies attractively and sorted by the level of relevance. Below is the complete list of available movies, along with their features. Describe these attributes in an appealing manner, commencing with the movie names. The total list of movies includes: [ { "Movie Title": "Daring Odyssey", "Category": "Adventure", "Description": "Embark on an exciting journey filled with twists and turns. Join the protagonists as they navigate through breathtaking landscapes and face thrilling challenges. A cinematic adventure you won't want to miss!", }, ... (whole list)...]"""
prompt2 = """You are a language model that recommends movies. Your job is to determine if the movie category and description are both suitable for the user's freely written description: ---- User's description: {user_description}. Movie category: {movie_category} Movie description: {movie_description} ---- Reply with a JSON object in this format: "reasoning": Briefly explain your reasoning to the user. "match": "YES" | "NO"
"""3) Performance Evaluation
Example of using LangSmith for evaluating LLM performance:
# Implementing a criteria evaluator for a test dataset
criteria_evaluator = """
# Sample evaluator to perform the test for each sample in our dataset
def evaluate_sample(sample):
# Evaluate the sample and return the score
# ...
return score
"""
# Running the test for each sample in a dataset using LangSmith client
langsmith_client_code = """
# LangSmith client code to run the test on a previously created key-value dataset
# ...
"""
# Checking LangSmith’s Web UI for the trace of the run for one specific sample
langsmith_web_ui = """
# LangSmith’s Web UI registers the trace of the run for one specific sample
# ...
"""4) Security Risks
Example of maintaining strict control over input and output:
# Integrating an input filter to validate and categorize user input
def input_filter(user_input):
# Validate and categorize user input
# ...
return categorized_inputBy following these examples and insights, you can effectively address challenges and ensure reliable and efficient AI text applications. The various code snippets provided demonstrate the practical implementation of solutions to enhance the usability and performance of Large Language Models for text generation.
