Target Audience: Data Specialists, IT Risk Management Officers.
The most significant hurdle for enterprise AI adoption is “AI Hallucination”—the phenomenon where large language models generate false information with high confidence. CustomGPT.ai tackles this through advanced Retrieval-Augmented Generation (RAG) architecture.
How It Works:
- Indexing & Vectorization: Upon uploading files, the system splits content into distinct chunks and converts them into vector embeddings stored within a Vector Database.
- Precise Retrieval: When a prompt is submitted, the system scans the internal database to retrieve the most semantically relevant text segments.
- Controlled Generation: The LLM acts solely as a synthesizer, forming responses using strictly the retrieved context.
Through this methodology, CustomGPT.ai delivers high precision required in enterprise environments where factual inaccuracies could result in legal or financial liabilities.
