Let's go through these categories and for each one identify key concepts (to clarify terminology) as well as the problems in this area LangChain helps solve.
**LLMs and Prompts**
Calling out to an LLM once is pretty easy, with most of them being behind well documented APIs.
However, there are still some challenges going from that to an application running in production that LangChain attempts to address.
*Key Concepts*
- LLM: A large language model, in particular a text-to-text model.
- Prompt: The input to a language model. Typically this is not simply a hardcoded string but rather a combination of a template, some examples, and user input.
- Prompt Template: An object responsible for constructing the final prompt to pass to a LLM.
*Problems solved*
- Switching costs: by exposing a standard interface for all the top LLM providers, LangChain makes it easy to switch from one provider to another, whether it be for production use cases or just for testing stuff out.
- Prompt management: managing your prompts is easy when you only have one simple one, but can get tricky when you have a bunch or when they start to get more complex. LangChain provides a standard way for storing, constructing, and referencing prompts.
- Prompt optimization: despite the underlying models getting better and better, there is still currently a need for carefully constructing prompts.
**Chains**
Using an LLM in isolation is fine for some simple applications, but many more complex ones require chaining LLMs - either with eachother or with other experts.
LangChain provides several parts to help with that.
*Key Concepts*
- Tools: APIs designed for assisting with a particular use case (search, databases, Python REPL, etc). Prompt templates, LLMs, and chains can also be considered tools.
- Chains: A combination of multiple tools in a deterministic manner.
*Problems solved*
- Standard interface for working with Chains
- Easy way to construct chains of LLMs
- Lots of integrations with other tools that you may want to use in conjunction with LLMs
- End-to-end chains for common workflows (database question/answer, recursive summarization, etc)
**Agents**
Some applications will require not just a predetermined chain of calls to LLMs/other tools, but potentially an unknown chain that depends on the user input.
In these types of chains, there is a “agent” which has access to a suite of tools.
Depending on the user input, the agent can then decide which, if any, of these tools to call.
*Key Concepts*
- Tools: same as above.
- Agent: An LLM-powered class responsible for determining which tools to use and in what order.
Goes over a simple walk through and tutorial for getting started setting up a simple chain that generates a company name based on what the company makes.
Covers installation, environment set up, calling LLMs, and using prompts.
