| # Glossary | |
| This is a collection of terminology commonly used when developing LLM applications. | |
| It contains reference to external papers or sources where the concept was first introduced, | |
| as well as to places in LangChain where the concept is used. | |
| ## 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. | |
| Resources: | |
| - [Chain-of-Thought Paper](https://arxiv.org/pdf/2201.11903.pdf) | |
| - [Step-by-Step Paper](https://arxiv.org/abs/2112.00114) | |
| ## 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](https://arxiv.org/pdf/2112.09332.pdf) | |
| - [SayCan Paper](https://say-can.github.io/assets/palm_saycan.pdf) | |
| ## ReAct Prompting | |
| 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. | |
| Resources: | |
| - [Paper](https://arxiv.org/pdf/2210.03629.pdf) | |
| - [LangChain Example](./modules/agents/implementations/react.ipynb) | |
| ## Self-ask | |
| 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: | |
| - [Paper](https://ofir.io/self-ask.pdf) | |
| - [LangChain Example](./modules/agents/implementations/self_ask_with_search.ipynb) | |
| ## Prompt Chaining | |
| Combining multiple LLM calls together, with the output of one-step being the input to the next. | |
| Resources: | |
| - [PromptChainer Paper](https://arxiv.org/pdf/2203.06566.pdf) | |
| - [Language Model Cascades](https://arxiv.org/abs/2207.10342) | |
| - [ICE Primer Book](https://primer.ought.org/) | |
| - [Socratic Models](https://socraticmodels.github.io/) | |
| ## Memetic Proxy | |
| 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](https://arxiv.org/pdf/2102.07350.pdf) | |
| ## 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](https://arxiv.org/pdf/2203.11171.pdf) | |
| ## 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](https://twitter.com/goodside/status/1583262455207460865?s=20&t=8Hz7XBnK1OF8siQrxxCIGQ) | |
| ## MemPrompt | |
| MemPrompt maintains a memory of errors and user feedback, and uses them to prevent repetition of mistakes. | |
| Resources: | |
| - [Paper](https://memprompt.com/) | |