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Malik Abualzait
Malik Abualzait

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Code with Context: The Missing Ingredient in AI Development

#ai #tech #programming #tutorial

Context Engineering Is a Must

Context Engineering: The Next Evolution of AI

In the rapidly evolving landscape of artificial intelligence, a new discipline has emerged that separates those who simply use AI tools from those who truly harness their power. Context engineering is the art and science of designing, constructing, and optimizing the information environment in which an AI model operates.

What is Context Engineering?

Context engineering goes far beyond crafting clever prompts; it encompasses the entire ecosystem of data, instructions, examples, and constraints that shape an AI's understanding and outputs. It involves:

  • Data curation: selecting and preparing high-quality training data for your AI models
  • Instruction design: creating clear and concise instructions that guide the AI's behavior
  • Example engineering: crafting thoughtfully designed examples to illustrate complex concepts and patterns
  • Constraint setting: establishing limits on what an AI is allowed to do, in order to prevent overfitting or undesirable outputs

Practical Implementation of Context Engineering

To put context engineering into practice, let's consider a real-world example: building a conversational AI assistant for customer service.

Step 1: Data Curation

  • Collect relevant customer data from support tickets, surveys, and other sources
  • Use tools like pandas and NumPy to clean and preprocess the data 
import pandas as pd

# load customer data into a Pandas dataframe
data = pd.read_csv('customer_data.csv')

# remove duplicates and irrelevant columns
data.drop_duplicates(inplace=True)
data.drop(columns=['unneeded_column'], inplace=True)

# encode categorical variables
data['category'] = data['category'].astype('category')
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Step 2: Instruction Design

  • Define clear guidelines for the AI to follow when responding to customer inquiries
  • Use natural language processing (NLP) techniques like entity recognition and intent identification to guide the conversation 
import nltk

# define a dictionary of possible intents and their corresponding responses
intents = {
    'greeting': ['Hello!', 'Hi!'],
    'problem_statement': ['I have an issue with my product.', 'My order was lost.']
}

# use NLTK to extract entities from customer input
text = "I'd like to return this shirt because it's too small."
entities = nltk.word_tokenize(text)
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Step 3: Example Engineering

  • Create a set of example dialogues that demonstrate how the AI should respond in different scenarios
  • Use these examples to fine-tune the model and ensure it's learning from realistic data 
import torch

# define a PyTorch dataset class for our example dialogues
class DialogueDataset(torch.utils.data.Dataset):
    def __init__(self, dialogues):
        self.dialogues = dialogues

    def __getitem__(self, idx):
        return {
            'input': dialogues[idx]['user_input'],
            'output': dialogues[idx]['model_output']
        }

# create a dataset and data loader for our example dialogues
dataset = DialogueDataset(dialogues)
loader = torch.utils.data.DataLoader(dataset, batch_size=32, shuffle=True)
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Step 4: Constraint Setting

  • Establish limits on what the AI is allowed to do, in order to prevent overfitting or undesirable outputs
  • Use techniques like regularization and early stopping to keep the model's predictions within a desired range 
import torch.nn as nn

# define a PyTorch model with L2 regularization
class Model(nn.Module):
    def __init__(self):
        super(Model, self).__init__()
        self.fc = nn.Linear(128, 10)
        self.dropout = nn.Dropout(p=0.1)

    def forward(self, x):
        return self.fc(x) + self.dropout(x)

# train the model with L2 regularization
criterion = nn.MSELoss()
optimizer = torch.optim.Adam(model.parameters(), lr=0.001, weight_decay=0.01)
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Best Practices for Context Engineering

To get the most out of context engineering, keep the following best practices in mind:

  • Keep it simple: focus on clear and concise instructions, rather than trying to cram too much complexity into your AI model
  • Use high-quality data: select relevant and well-curated training data that accurately represents real-world scenarios
  • Iterate and refine: continually test and refine your context engineering efforts to ensure they're having the desired impact on your AI system's performance

By following these best practices and incorporating context engineering into your AI development workflow, you'll be able to unlock the full potential of large language models and other AI systems.

By Malik Abualzait

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