What is RAG?
Retrieval Augmented Generation (RAG) is the advanced technique that personalizes AI by connecting models like GPT-4 to your own data, allowing them to provide accurate answers based YOUR information.
Understanding RAG Technology
What makes RAG powerful?
RAG is a technique that augments LLM knowledge with data not present on the public internet during its pretraining. While an LLM's knowledge is "frozen" at its training cutoff date, RAG enables it to access and utilize your personal or proprietary data.
RAG converts your data into tokens and vector embeddings—essentially translating them into the mathametical language AI can understand. This allows the model to find the precise information most closely related to your query / question even if the context for the answer exists across a massive amount of documents.
The more specific and well-formulated your question, the better the system can map it to the relevant "nearest neighbor" vectors containing the answer.
How RAG Works
1. Document Processing
Your documents or scraped content from a URL are broken down into chunks and processed to preserve their structure and meaning.
2. Vectorization
Each chunk is converted into a numerical representation (vector) that captures its meaning and stored in a specialized database.
3. Retrieval
When you ask a question, your question is converted to a vector, so AI can find the most similar document chunks/vectors in the database using semantic search.
4. Generation
The retrieved information is fed to the LLM as context to answer your question, which then generates an answer based on the retrieved information.
5. Citation
The system provides links to the original sources used to answer your question, giving you direct access to the original source material.
6. Continuous Learning
As your collections grow over time with new documents vectorized to your vector database, your AI will grow in knowledge relative to the size of your "AI library".
Expert Insight: Why RAG Matters
Andrej Karpathy, former Director of AI at Tesla and a leading AI researcher, explains a critical limitation of LLMs:
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.
When Do You Need a RAG Platform?
- 1
Large Documents
When dealing with documents too large for the AI model's context window.
- 2
Multiple Documents
When searching for answers across multiple documents simultaneously.
- 3
Long-term Memory
When requiring AI to remember all your information over time.
- 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 wanting to share your knowledge as a chat interface others can simply query for answers.
- 6
Prevent Hallucinations
When you need AI to source its answers from your data.
Key Benefits of RAG
Enhanced Accuracy
RAG (with AskMyAI's guardrails) eliminates hallucinations by grounding AI responses in your actual data. If the information isn't in your documents, the LLM will tell you instead of making things up.
Up-to-date Information
Unlike standard LLMs that are limited to their training data cutoff, RAG systems can access the latest information you've added to your collection, always staying current.
Specialized Knowledge
Access domain-specific or proprietary information that general AI models don't have. This includes internal documents created in your org as well as information you consume and want to save for future retrieval like content from web articles, youtube videos, social media posts, and more.
Data Security
Keep sensitive information within your control. With AskMyAI's platform, your data is securely encrypted and never used to train other models or sold to third parties.
RAG vs. Direct LLM Use
| Feature | Using RAG | Direct LLM Use |
|---|---|---|
| Knowledge Source | Your own data + LLM's knowledge | Only public internet knowledge |
| Document Size Handling | Can process documents of any size | Limited by context window |
| Multiple Document Processing | Can analyze across thousands of documents | Limited to a couple small docs at once |
| File Format Support | Wide range of formats including Google formatted files like Docs, Sheets, Slides, and MS Office files like Word, Excel, PowerPoint, Kindle, large video / audio files, and more. | Limited format support |
| Hallucination Risk | Zero risk (with AskMyAI's guardrails) | High risk |
| Source Citations | Links provided to original source content | Often unavailable |
| URL Scraping | AskMyAI's ETL engine scrapes and parses clean data from any non-gated URL | Very limited capabilities |
| Memory | Long-term memory (knowledge is permanently stored) | Ephemeral (one-time use only) |
Why Choose AskMyAI's RAG Platform
Advanced RAG Techniques
AskMyAI 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 3 ways: (1) with other AskMyAI users, (2) via a dedicated URL (hosted by AskMyAI) that anyone can access, or (3) embed as a chatbot on your website or mobile app with just a few lines of code.
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 folders to automatically sync all files with an AskMyAI collection, ensuring your AI always has access to the latest information.
Web Scraping
Our platform features advanced web scraping with a 5-step agentic process to extract clean, structured content from nearly any non-gated URL, YouTube video, or social media post.
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
AskMyAI'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.
Actions
Once you bind your knowledge and business data to a LLM via the AskMyAI platform, you are ready for the next step to bind tools and functions to the LLM to automate specific tasks. The AskMyAI Actions Builder tool makes it easy to rapidly build multi-agent workflows to put AI to work in your business.