LangChain Glossary Glossary — 🦜🔗 LangChain 0.0.120
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Chain of Thought Prompting
- A prompting technique used to encourage the model to generate a series of intermediate reasoning steps. A less formal way to induce this behavior is to include “Let’s think step-by-step” in the prompt.
- Chain of Thought
- Step-by-Step(LLM)
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Action Plan Generation - Action Plan Generation
- A prompt usage that uses a language model to generate actions to take. The results of these actions can then be fed back into the language model to generate a subsequent action.
- Resources:
- WebGPT Paper
- SayCan Paper
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ReAct Prompting
- Chain of Thought combined with [Action Plan Generation
- A prompting technique that combines Chain-of-Thought prompting with action plan generation. This induces the to model to think about what action to take, then take it.
- ReAct(LLM)
- LangChain Example
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Self-ask
- Explicitly have question text generated and use an external search engine
- A prompting method that builds on top of chain-of-thought prompting. In this method, the model explicitly asks itself follow-up questions, which are then answered by an external search engine.
- Resources:
- self-ask
- LangChain Example
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Prompt Chaining
- Combining multiple LLM calls together, with the output of one-step being the input to the next.
- Resources:
- PromptChainer Paper
- Language Model Cascades
- ICE Primer Book
- Socratic Models
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Memetic Proxy
- Make them behave in a specific context by having them play a role.
- Encouraging the LLM to respond in a certain way framing the discussion in a context that the model knows of and that will result in that type of response. For example, as a conversation between a student and a teacher.
- Resources:
- Paper
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Self Consistency
- A decoding strategy that samples a diverse set of reasoning paths and then selects the most consistent answer. Is most effective when combined with Chain-of-thought prompting.
- Resources:
- Paper
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Inception
- Also called “First Person Instruction”. Encouraging the model to think a certain way by including the start of the model’s response in the prompt.
- Resources:
- Example
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MemPrompt
- MemPrompt maintains a memory of errors and user feedback, and uses them to prevent repetition of mistakes.
- Resources:
- Paper
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