Frontier Models vs RAG
Your AI is brilliant... but blind to your business.
Learn how frontier models work, their limitations, and how RAG bridges the gap.
175B+
Parameters
~2023
Knowledge Cutoff
0
Access to Your Data
∞
Potential with RAG
How Frontier Models Work
ChatGPT, Claude, and other large language models are transformer-based neural networks trained on massive text datasets. Here's the magic under the hood.
Input Text
Tokenizer
Embeddings
Attention Layers
Output
User Input
What is machine learning?
The Knowledge Limitation
Frontier models are frozen in time. They can only know what existed in their training data— and they have zero knowledge of your proprietary information.
Knowledge Cutoff
Question:
Who won the 2022 World Cup?
Response:
In training dataArgentina won the 2022 FIFA World Cup, defeating France in the final.
Click on 2022, 2024, or 2026 to see different responses
Hallucination Demo
What was Acme Corp's Q3 2025 revenue?
Example 1 of 3 · Click refresh for more
Enter RAG: The Solution
Retrieval-Augmented Generation (RAG) bridges the knowledge gap by retrieving relevant information and injecting it into the model's context before generation.
Query:
What are the key features of our enterprise product?
User Query
LLM Processing
Response
Toggle between Pure LLM and RAG to see the difference
RAG Components Deep Dive
RAG systems combine multiple retrieval strategies. Explore each component to understand how they work together.
Vector Embedding Space
How it works: Documents are converted to high-dimensional vectors (1536 dims), then projected to 3D for visualization.
Similar documents cluster together. When you search, the query becomes a vector and finds nearby documents in this space.
Putting It All Together
Watch the complete RAG pipeline in action—from user query to grounded, accurate response.
How does our enterprise product handle data security?
Press play to watch the complete RAG pipeline in action
6 steps · ~8 seconds total
SemanticStudio
Everything you just learned—implemented in a production-ready, open-source platform. 28 domain agents, 4-tier memory, GraphRAG-lite, and self-learning ETL.
Now you understand the foundations. Ready to see them in action?