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Start With Smaller Context Windows
Test models with smaller context windows first to save on API costs during development.
Hugging Face
Hugging Face is a central hub for the AI community. It provides access to a massive library of open-source models (Transformers), datasets, and tools for building, training, and deploying machine learning models. It is an essential resource for any AI developer.
Andrej Karpathy on Data
""The data is the source code.""
AI Can Write Code
Many LLMs are proficient at generating code in various programming languages, debugging, explaining code snippets, and even translating code between languages. However, generated code always requires careful review.
Weights & Biases (W&B)
A popular MLOps platform for tracking experiments, visualizing model performance, and managing machine learning workflows. Very useful for serious AI development and research.
Training LLMs is Expensive
Training state-of-the-art large language models requires massive datasets, significant computational power (often thousands of GPUs), and can cost millions of dollars.
TensorFlow & PyTorch
These are foundational open-source machine learning frameworks used for building and training deep learning models, including many LLMs.
LangChain Framework
LangChain is a framework for developing applications powered by language models. It provides modular components for managing prompts, chains, memory, and agents.
Character Development
Ask LLMs to help develop fictional characters by generating backstories, personality traits, motivations, and character arcs based on your initial concepts.
Mock Data Generation
Generate realistic mock data for testing applications, including user profiles, transaction records, and sample content that matches your schema.
Skill Gap Analysis
Ask LLMs to analyze your current skills against job requirements or learning goals, and suggest a personalized learning path to bridge gaps.
Quote
"The development of full artificial intelligence could spell the end of the human race."
Building a Prompt Template System
Create reusable prompt templates: 1) Identify common prompt patterns in your application, 2) Extract variable parts into placeholders, 3) Create template functions with parameter validation, 4) Build a library of tested templates, 5) Implement version control for template changes.
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