Integrating LangChain with JavaScript for Web Developers

RAG stands for Retrieval Augmented Generation, a technique in prompt engineering that involves writing a big prompt with step-by-step instructions and examples, then extracting the relevant context only when needed. This helps in reducing the context length and cost of using Large Language Models (LLMs).

Context length is important because LLMs have a token limit, which means you cannot feed them excessively long prompts. Shortening the context length helps in reducing the cost of each call to an LLM and ensures that the prompt fits within the model's token limit.

You can integrate a PDF or Slack channel history with an AI LLM by using Retrieval Augmented Generation (RAG). This involves extracting only the relevant context from the entire history or document and then passing that to the LLM for generating the response.

The workshop covers Zero-shot, Few-shot, Chain of Thought, and Retrieval Augmented Generation (RAG) prompt engineering techniques.

The OpenAI API key should be stored in an environment file, commonly named .env or env.local, which should be in the root folder of your project.

Vector Databases in Lanchain are used to store embeddings, which are vector representations of chunks of documents or queries. This specialized database allows for efficient retrieval of relevant context when querying large, unstructured datasets.

You can create structured output from an LLM in Lanchain by defining a schema using libraries like Zod. This schema helps in formatting the output as a JSON or any other structured format.

Embeddings in Lanchain represent chunks of documents or queries in a vector space. These embeddings are used to perform semantic searches to find relevant context or answers from large datasets.

The 'system message' in prompt templates sets initial instructions for the LLM, guiding how it should respond to user queries. For example, it can make the LLM answer questions as a specific character or follow particular guidelines.

Creating a RAG-based application involves: 1) Loading the document, 2) Splitting the document into smaller chunks, 3) Creating embeddings of these chunks, 4) Storing these embeddings in a Vector Database, 5) Retrieving relevant context based on a query, and 6) Passing this context to an LLM to generate the final answer.