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Harrison/improve data augmented generation docs (#390)

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Co-authored-by: cameronccohen <cameron.c.cohen@gmail.com>
Co-authored-by: Cameron Cohen <cameron.cohen@quantco.com>
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Harrison Chase
2022-12-20 22:24:08 -05:00
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@@ -28,7 +28,7 @@ Please see [here](https://langchain.readthedocs.io/en/latest/?) for full documen
## 🚀 What can this help with?
There are four main areas that LangChain is designed to help with.
There are five main areas that LangChain is designed to help with.
These are, in increasing order of complexity:
**📃 LLMs and Prompts:**
@@ -39,6 +39,10 @@ This includes prompt management, prompt optimization, generic interface for all
Chains go beyond just a single LLM call, and are sequences of calls (whether to an LLM or a different utility). LangChain provides a standard interface for chains, lots of integrations with other tools, and end-to-end chains for common applications.
**📚 Data Augmented Generation:**
Data Augmented Generation involves specific types of chains that first interact with an external datasource to fetch data to use in the generation step. Examples of this include summarization of long pieces of text and question/answering over specific data sources.
**🤖 Agents:**
Agents involve an LLM making decisions about which Actions to take, taking that Action, seeing an Observation, and repeating that until done. LangChain provides a standard interface for agents, a selection of agents to choose from, and examples of end to end agents.