RAG Simply Explained
Retrieval-Augmented Generation (RAG) is an advanced technique that personalizes AI by connecting models like ChatGPT to your own data, enabling them to deliver accurate, context-aware answers based on your information.
Understanding RAG
What makes RAG powerful?
Retrieval-Augmented Generation (RAG) is a technique that extends an LLM’s knowledge beyond what was available during its pretraining by integrating data that does not exist on the public internet. While an LLM’s knowledge is effectively “frozen” at its training cutoff date, RAG enables it to securely access and leverage your personal or proprietary data at query time.
RAG transforms your data into tokens and vector embeddings—translating it into the mathematical representation AI models can understand. This allows the system to efficiently locate the most relevant information related to your question, even when the necessary context is distributed across large volumes of documents.
The more specific and well-structured your question, the more effectively the system can map it to the nearest-neighbor vectors that contain the most accurate and relevant answer.
How RAG Works
Document Processing
Your documents are broken down into chunks and processed in such a way to preserve their original structure.
Vector Embeddings
Each document chunk is converted into a numerical address (vector) derived from the relationship between words and phrases in the chunk.
Retrieval
When you ask a question, AI uses proximity between vectors in high-dimensional space to find the right answer. This is called semantic search.
Generation
The retrieved information is injected into the LLM's context window, enabling it to generate an answer grounded in that information.
Expert Insight: The Importance of RAG
Andrej Karpathy, the world's most renowned AI researcher, explains the fundamental limitation of LLMs that requires RAG for domain-specific knowledge:
Even if a book was part of an LLM's pretraining data, the model's memory of specific details in chapters will be hazy at best and likely to produce hallucinations. The solution is to augment the model's knowledge by providing it with the original text from a chapter.
This is why RAG techniques are so important and powerful—they augment the LLM's knowledge, ensuring the LLM has the exact information needed to generate accurate responses even if the data is scattered across a large volume of documents.
When Do You Need RAG?
- 1
Specialized Knowledge
When you need the AI model to know information it was not trained on.
- 2
Fresh Knowledge
When your information changes frequently and AI must access current data.
- 3
Large Volume of Data
When the answer must be found across a large volume of data that is too large for the context window of any LLM.
- 4
Diverse File Formats
When using file formats not natively supported by LLMs (Google formatted files, MS Office files, Kindle, large video / audio files, etc.).
- 5
Knowledge Sharing
When needing to share specific domain knowledge as an AI chat interface others can query for answers.
- 6
Source Attribution
When you need AI to source its answers from your information and cite the document.
Why Choose AskBnB's RAG Platform
Advanced RAG Techniques
AskBnB has developed advanced pre-processing and post-processing techniques that significantly enhance RAG performance. Our platform maintains context continuity across document chunks while preserving original structure and adding metadata for smart filtering.
Share Collections
Collections can be shared to a URL fully hosted by AskBnB that anyone can access where your knowledge is transformed into an AI chat experience.
Broad File Format Support
Support for the broadest range of file formats including Google Docs/Sheets/Slides, MS Office, Kindle, video/audio files, complex PDFs, multi-tab XLSX files, and even web URLs and social media posts.
Google Drive / OneDrive Integration
Connect Google Drive or Microsoft OneDrive files to automatically sync changes with your AskBnB collection, ensuring your AI always has access to the latest information.
Data Security
SOC 2 Type II, HIPAA, and GDPR compliant with end-to-end encryption. Your data is never sold or used to train other models.
Zero Hallucinations
AskBnB's guardrails ensure AI sources its answers only from the data you vectorized to your "AI library" and never hallucinates. Links to the source file that AI used as context for its answer are provided.