In the world of Large Language Models, hallucinations have always been the elephant in the room. Enter RAG — a powerful hybrid architecture that combines the reasoning capabilities of LLMs with the factual grounding of external knowledge sources.
How it works: RAG augments the prompt pipeline by:
- Retrieving relevant documents from a vector database (like FAISS or Weaviate)
- Feeding that context into the LLM to generate more accurate, grounded responses
- Benefit: Better factual accuracy, Reduced hallucination, Scalable to enterprise knowledge systems
Use Cases:
- Enterprise search assistants
- Legal & compliance document summarization
- Context-aware customer support bots
- Internal Q&A over proprietary data
we’re already integrating RAG pipelines into custom AI solutions, combining LangChain, OpenAI (or LLaMA), and vector stores to solve real business problems.
Curious how RAG can supercharge your AI stack? Let’s connect.
