Retrieval Augmented Generation for LLM Bots with LangChain
Blog post from Activeloop
Retrieval Augmented Generation (RAG) is an advanced AI technique that combines information retrieval and text generation to handle complex knowledge-intensive tasks. It searches for relevant documents from specified sources, integrates this data with the input, and provides a comprehensive output with references. RAG has shown significant potential in boosting productivity across various industries, including customer support and sales. Its future points towards the development of Knowledge Assistants that can interact with enterprise systems and take action on behalf of workers. Adoption of RAG and LLMs is crucial for businesses to maintain a competitive edge in today's rapidly-evolving digital landscape.
| Trend | Post Mentions | Total Month Mentions | Posts | Companies | MoM |
|---|---|---|---|---|---|
| RAG | 54 | 267 | 69 | 29 | +85% |
| LLM | 22 | 3,077 | 361 | 126 | +59% |
| Vector Search | 11 | 1,841 | 251 | 82 | +59% |
| Secrets Management | 9 | 817 | 130 | 67 | -40% |
| AI Model Fine-tuning | 1 | 670 | 134 | 68 | +0% |
| Real-time | 1 | 2,542 | 668 | 195 | +25% |
| Serverless | 1 | 871 | 162 | 80 | -5% |
Use this post, company, and trend context to find content marketing opportunities, perform competitive analysis, or address product feature gaps via the Plushcap MCP server or the Plushcap API.