From Claude with some prompting
This diagram titled “Chain of thoughts” illustrates an inferencing method implemented in AI language models like ChatGPT, inspired by human deductive reasoning processes and leveraging prompting techniques.
Key components:
- Upper section:
- Shows a process from ‘Q’ (question) to ‘A’ (answer).
- Contains an “Experienced Knowledges” area with interconnected nodes A through H, representing the AI’s knowledge base.
- Lower section:
- Compares “1x Prompting” with “Prompting Chains”.
- “1x Prompting” depicts a simple input-output process.
- “Prompting Chains” shows a multi-step reasoning process.
- Overall process:
- Labeled “Inferencing by <Chain of thoughts>”, emphasizing the use of sequential thinking for complex reasoning.
This diagram visualizes how AI systems, particularly models like ChatGPT, go beyond simple input-output relationships. It mimics human deductive reasoning by using a multi-step thought process (Chain of thoughts) to answer complex questions. The AI utilizes its existing knowledge base and creates new connections to perform deeper reasoning.
This approach suggests that AI can process information and generate new insights in a manner similar to human cognition, rather than merely reproducing learned information. It demonstrates the AI’s capability to engage in more sophisticated problem-solving and analysis through a structured chain of thoughts.
Lechuck Park 