Training an AI model involves teaching it patterns from large datasets to generate responses, while grounding with Retrieval-Augmented Generation (RAG) uses external knowledge sources at query time to provide accurate, context-specific answers without retraining the model.
What does “training” an AI model mean?
Training is the process where an AI learns from massive datasets—text, images, code—to understand language patterns and generate coherent outputs. This typically happens once or periodically and requires substantial computing power.
What does training involve?
- Feeding labeled or unlabeled data
- Adjusting millions of parameters
- Creating a generalized language understanding
- Often done by AI providers (e.g., OpenAI, Google)
Limitations of training
- Time-consuming and costly
- Model knowledge can become outdated
- Difficult to incorporate new, proprietary data quickly
Key takeaway
Training builds the core intelligence of an AI but is static until retrained.
What is “grounding” with Retrieval-Augmented Generation (RAG)?
RAG combines a pretrained AI model with a dynamic retrieval system that fetches relevant documents or data at the moment of query. It grounds AI answers in real, up-to-date information rather than relying solely on the model’s training.
How does grounding work?
- User asks a question
- Retrieval system searches private or public databases for relevant info
- AI model generates an answer based on retrieved documents
Benefits of RAG grounding
- Always current answers without retraining
- Incorporates proprietary or sensitive knowledge securely
- Reduces hallucinations by basing responses on real data
Key takeaway
Grounding via RAG keeps AI responses accurate and context-specific dynamically.
When to use training vs grounding?
| Aspect | Training | Grounding (RAG) |
|---|---|---|
| Data incorporation | Large-scale, general data | Specific, up-to-date knowledge |
| Update frequency | Periodic retraining needed | Real-time knowledge retrieval |
| Cost | High computational cost | Lower, as no full retraining required |
| Accuracy | General knowledge, risk of hallucinations | Accurate, sourced answers |
| Use cases | Building foundational AI models | Specialized AI assistants using proprietary data |
Gartner predicts that by 2026, 70% of enterprise AI deployments will rely on RAG methods to ensure accuracy and relevance.
How does CustomGPT use training and grounding?
CustomGPT approach
- Uses pretrained language models as the base intelligence
- Grounds answers with your company’s documents, manuals, and FAQs in real-time
- Allows instant updates without costly retraining
- Ensures responses are both fluent and factually accurate
Example
A user asks about a new company policy:
- CustomGPT retrieves the latest policy document
- Generates a precise answer based on actual content, not outdated training data
Key takeaway
CustomGPT leverages the best of both worlds. It is a powerful AI with dynamic, accurate grounding.
Summary
Training an AI model creates its foundational language abilities, while grounding with RAG dynamically ties AI answers to current, specific data. Combining both ensures intelligent, trustworthy AI assistants that evolve with your knowledge base.
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Frequently Asked Questions
What is the difference between training an AI model and grounding it with RAG?
Training an AI model teaches it general language patterns using large datasets, while grounding with Retrieval-Augmented Generation connects the model to external knowledge sources at the time of a query. Training defines what the model can do in general, whereas grounding ensures responses are accurate, current, and specific to the data being referenced.
What does training an AI model mean?
Training is the process of exposing an AI model to massive volumes of data so it learns how language, concepts, and patterns work. This process adjusts millions of internal parameters and results in a generalized model capable of generating human-like responses across many topics.
Why is AI training expensive and infrequent?
AI training requires large datasets, specialized hardware, and significant time to complete. Because of the cost and complexity, models are typically trained periodically rather than continuously, which means their internal knowledge can become outdated over time.
What are the limitations of relying only on training?
Models that rely only on training cannot easily incorporate new or proprietary information. Their knowledge remains static until retraining occurs, which makes them less reliable for use cases that depend on up-to-date policies, documentation, or fast-changing data.
What does grounding with Retrieval-Augmented Generation mean?
Grounding with RAG means supplementing a pretrained AI model with a retrieval system that fetches relevant information at query time. Instead of answering solely from its internal knowledge, the AI bases its response on retrieved documents or data sources.
How does grounding with RAG work in practice?
When a user asks a question, the retrieval system searches connected knowledge sources for relevant information. The AI model then generates an answer using that retrieved content as context, ensuring the response reflects actual, current data.
Why does grounding reduce AI hallucinations?
Grounding reduces hallucinations because responses are anchored in retrieved documents rather than generated purely from learned patterns. This ensures answers are traceable to real information instead of inferred or assumed knowledge.
Can grounding be updated without retraining the model?
Yes. Grounding allows new documents or updates to be added instantly to the retrieval system. The AI can use this new information immediately without any need to retrain the underlying model.
When should training be used instead of grounding?
Training is appropriate when building foundational AI models that need broad, generalized language understanding. It is not designed for frequent updates or organization-specific knowledge.
When is grounding with RAG the better approach?
Grounding with RAG is the better approach when accuracy, freshness, and proprietary data matter. It is ideal for enterprise assistants, customer support bots, and internal tools that must reflect current information.
How does grounding support enterprise AI use cases?
Grounding allows enterprises to safely use private documents, manuals, and policies without embedding them into the model itself. This keeps sensitive data controlled while enabling accurate, real-time responses.
How does CustomGPT use training and grounding together?
CustomGPT uses pretrained language models for fluent responses and grounds them with company-specific documents at query time. This approach ensures answers are both linguistically strong and factually accurate without requiring costly retraining.
Does grounding replace the need for training?
No. Training and grounding serve different purposes. Training provides the foundational intelligence, while grounding ensures relevance and accuracy. The most effective AI systems combine both.
Why is RAG becoming the standard for enterprise AI?
RAG is becoming standard because it allows organizations to deploy accurate AI assistants quickly, keep information current, and reduce the risks associated with outdated or hallucinated responses.