This section delves into advanced guiding techniques for large language models, exploring methods like self-consistency, three of thought, and prompt chaining to unlock more powerful and nuanced outputs.
ART integrates reasoning and external tools, enhancing AI's problem-solving versatility across domains like healthcare, law, and supply chain.
RAG helps AI models retrieve relevant facts from external sources, reducing errors and making responses more grounded in reality.
Tree of Thought lets AI models explore multiple ideas, like branches on a tree, to avoid dead ends and find solutions more efficiently.
Learn about Prompt Chaining and try it in our AI Playground with your favourite models.
Explore a simple alternative to Generated Knowledge prompts
Supercharge your AI models with optimized prompts. Explore the power of generated knowledge and dual prompt strategy.
Self-consistency aims to "replace the naive greedy decoding used in chain-of-thought prompting"
In-depth analysis comparing AI models' performance across diverse scenarios, including code generation, visual processing, and multimedia content creation tasks.
What is LLM. How is AI Inference different from training. Is API a part of AI. We collected all the questions an AI novice can have. This is the FAQ for the beginners, your LLM Basics.
Discover how AI APIs power solutions across sectors by streamlining processes, enhancing analytics, automating tasks, and improving user experiences in real-time applications.
By structuring effective prompts, users can boost the capacity, understanding, enabling applications like content creation, question answering, code generation, and more.
Advanced prompting techniques like self-consistency and tree of thoughts unlock the full potential of the models. Dive into the state-of-the-art prompting with us.
Recent language models use advanced prompting techniques, including few-shot, zero-shot, and chain-of-thought prompting, to perform a variety of tasks.