// MODEL OPTIMIZATION AND PROMPT SYNTAX TERM
Chain of Thought
A technique where a large language model (LLM) explains its reasoning steps before giving a final answer, similar to how a human might think through a problem.
TECHNICAL DEFINITION
Chain of Thought (CoT) prompting is a technique that enables large language models (LLMs) to decompose multi-step problems into intermediate reasoning steps, improving complex reasoning abilities by explicitly generating an "inner monologue" or sequence of thoughts.
BACKGROUND
Prompt engineering is the process of structuring natural language inputs to produce specified outputs from a generative artificial intelligence (GenAI) model. Context engineering is the related area of software engineering that focuses on the management of non-prompt contexts supplied to the GenAI model, such as metadata, API tools, and tokens.
READ MORE ON WIKIPEDIASYNONYMS & ALIASES
- CoT
- Step-by-step reasoning
- Explanatory prompting
- Reasoning chain
USAGE NOTE
CoT is widely used to improve LLM performance on complex arithmetic, commonsense, and symbolic reasoning tasks.
DEVELOPERS
Organizations developing technology related to Chain of Thought.
Google AI's research teams, including those responsible for models like LaMDA and PaLM, were instrumental in introducing and advancing Chain of Thought prompting techniques to improve the reasoning capabilities of large language models.
As developers of the GPT series of models, OpenAI continuously explores and integrates advanced prompting techniques, including Chain of Thought, into their models and APIs to enhance performance and enable more complex reasoning tasks for AI engineers and prompt designers.
Anthropic, creator of the Claude series of large language models, focuses on developing reliable and interpretable AI. Their work on 'Constitutional AI' and advanced prompting often incorporates structured reasoning steps akin to or building upon Chain of Thought principles.
LangChain is a widely used framework for developing applications powered by large language models. It provides abstractions and tools for developers to implement complex prompt sequences, including Chain of Thought, to build multi-step reasoning and agentic behaviors.
LlamaIndex is a data framework for LLM applications, facilitating data ingestion, indexing, and retrieval augmented generation. It enables developers to structure information and prompt workflows to effectively leverage reasoning techniques like Chain of Thought for more accurate and context-aware responses.
Hugging Face provides open-source tools, libraries (like Transformers), and a platform for building, training, and deploying machine learning models. Their community and resources actively engage with and facilitate the exploration and implementation of prompt engineering techniques, including Chain of Thought, for various LLMs.
Microsoft Research actively conducts cutting-edge AI research, including advancements in large language models and prompt engineering. Their work often explores methods to improve model reasoning and reliability, frequently incorporating or iterating on concepts like Chain of Thought for various applications.