Retrieval-Augmented Generation (RAG)
RAG is a technique that enhances AI responses by retrieving relevant information from external data sources before generating an answer, reducing hallucinations and improving accuracy.
Retrieval-augmented generation combines the creative power of large language models with the factual accuracy of information retrieval systems. Instead of relying solely on what the model memorized during training, RAG first searches relevant documents, databases, or knowledge bases, then uses that retrieved information to generate a grounded response.
This approach solves one of the biggest challenges with generative AI: hallucination. When a model generates text purely from its training data, it can confidently state incorrect information. RAG anchors the generation in real, verifiable data - making it far more reliable for business-critical tasks.
AI coworkers use RAG extensively. When Cole prepares a meeting brief, it retrieves your actual CRM data, recent emails, and meeting notes rather than making things up. When answering a question about your company's policies, it pulls from your connected knowledge base rather than guessing. This is what makes AI agents trustworthy enough for real business work.
How Cole uses retrieval-augmented generation
Cole, Pocodot's AI coworker, leverages retrieval-augmented generation (rag) as part of its core capabilities. Working across 6 messaging channels with 3,500+ tool integrations, Cole applies these concepts to handle real business tasks - from email management and scheduling to research, follow-ups, and team coordination. Instead of learning the theory yourself, you get the practical benefits through natural conversation.
See Cole in actionRelated terms
Large Language Model (LLM)
A large language model (LLM) is a type of AI model trained on vast amounts of text data that can understand and generate human language with remarkable fluency.
AI Hallucination
An AI hallucination occurs when an AI model generates information that sounds plausible but is factually incorrect, fabricated, or not grounded in its input data.
Prompt Engineering
Prompt engineering is the practice of crafting inputs (prompts) to AI models to elicit desired outputs, maximizing accuracy, relevance, and usefulness of AI-generated responses.
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